Trajectories of Risk-taking Propensity (demo)
Yun-Rui Liu
1. Overview of Project
Insert Abstract
2. Introduction of the file
The following document contains results from all analyses conducted for the manuscript titled “Trajectories of Risk-taking Propensity: A Coordinated Analysis of Longitudinal Panels”. This document is organized by different domain risk-taking propensity,including general, driving, financial, recreational, occupational, health and social domain. For each risk-taking propensity, we create 7 models (including intercept-only model, fixed effect model, linear model, linear with gender model, linear with gender interaction model, quadratic model and quadratic with gender model) and provide a table summarizing individual study model results, trajectory plots, the meta-analysis results. We tested individual predictors that are not included in the simple trajectory model in meta regression: country, continent, mean age and scale range. And the results from these models are available below. The code used to compile this file is available here (insert link)
3. Overview of panel data
3.1. The number of participants
3.2. Histogram of age distributions (all observations)
3.3. Histograms and Density Plots for every panel
SOEP
Age distribution
Risk density
HRS
Age distribution
Risk density
SAVE
Age distribution
Risk density
DNB
Age distribution
Risk density
PHF
Age distribution
Risk density
SHARE_Austria
Age distribution
Risk density
SHARE_Germany
Age distribution
Risk density
SHARE_Sweden
Age distribution
Risk density
SHARE_Netherlands
Age distribution
Risk density
SHARE_Spain
Age distribution
Risk density
SHARE_Italy
Age distribution
Risk density
SHARE_France
Age distribution
Risk density
SHARE_Denmark
Age distribution
Risk density
SHARE_Switzerland
Age distribution
Risk density
SHARE_Belgium
Age distribution
Risk density
SHARE_Israel
Age distribution
Risk density
SHARE_Czech_Republic
Age distribution
Risk density
SHARE_Slovenia
Age distribution
Risk density
SHARE_Estonia
Age distribution
Risk density
Usoc
Age distribution
Risk density
GCOE_Japan
Age distribution
Risk density
GCOE_USA
Age distribution
Risk density
HILDA
Age distribution
Risk density
LIKS
Age distribution
Risk density
4. Multi-level model results
4.1. General risk-taking
Intercept only model
Models results
Fixed effect model
Models results
Linear model
Models results
Plot
Linear with gender model
Models results
Plot
Linear with gender interaction model
Models results
Plot
Quadratic model
Models results
Plot
Quadratic with gender model
Models results
Plot
4.2. Driving risk-taking
Intercept only model
Models results
Fixed effect model
Models results
Linear model
Models results
Plot
Linear with gender model
Models results
Plot
Linear with gender interaction model
Models results
Plot
4.3. Financial risk-taking
Intercept only model
Models results
Fixed effect model
Models results
Linear model
Models results
Plot
Linear with gender model
Models results
Plot
Linear with gender interaction model
Models results
Plot
Quadratic model
Models results
Plot
Quadratic with gender model
Models results
Plot
4.4. Recreational risk-taking
Intercept only model
Models results
Fixed effect model
Models results
Linear model
Models results
Plot
Linear with gender model
Models results
Plot
Linear with gender interaction model
Models results
Plot
4.5. Occupational risk-taking
Intercept only model
Models results
Fixed effect model
Models results
Linear model
Models results
Plot
Linear with gender model
Models results
Plot
Linear with gender interaction model
Models results
Plot
4.6. Health risk-taking
Intercept only model
Models results
Fixed effect model
Models results
Linear model
Models results
Plot
Linear with gender model
Models results
Plot
Linear with gender interaction model
Models results
Plot
5. Meta-analysis results
5.1. General risk-taking
Intercept only model
Meta analysis
ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9766 -7.9532 -3.9532 -4.3697 0.0468
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1246
## I^2 (total heterogeneity / total variability): 99.83%
## H^2 (total variability / sampling variability): 582.81
##
## Test for Heterogeneity:
## Q(df = 6) = 3633.8026, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4330 0.0472 9.1660 <.0001 0.3404 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
ICC’s results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9112 -5.8225 2.1775 -0.2773 42.1775
##
## tau^2 (estimated amount of residual heterogeneity): 0.0136 (SE = 0.0097)
## tau (square root of estimated tau^2 value): 0.1166
## I^2 (residual heterogeneity / unaccounted variability): 99.68%
## H^2 (unaccounted variability / sampling variability): 316.66
## R^2 (amount of heterogeneity accounted for): 12.31%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 1457.3211, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.8299, p-val = 0.2429
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3250 0.0826 3.9351 <.0001 0.1631 0.4868 ***
## continentEurope 0.1798 0.1069 1.6821 0.0926 -0.0297 0.3892 .
## continentNorth America 0.1096 0.1168 0.9383 0.3481 -0.1193 0.3385
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
ICC’s results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6056 -7.2112 -1.2112 -2.3829 22.7888
##
## tau^2 (estimated amount of residual heterogeneity): 0.0138 (SE = 0.0088)
## tau (square root of estimated tau^2 value): 0.1175
## I^2 (residual heterogeneity / unaccounted variability): 99.80%
## H^2 (unaccounted variability / sampling variability): 490.35
## R^2 (amount of heterogeneity accounted for): 11.08%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 3487.6720, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7437, p-val = 0.1867
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0226 0.3139 0.0721 0.9425 -0.5926 0.6379
## mean.age 0.0080 0.0061 1.3205 0.1867 -0.0039 0.0199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
ICC’s results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9766 -7.9532 -3.9532 -4.3697 0.0468
##
## tau^2 (estimated amount of total heterogeneity): 0.0155 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1246
## I^2 (total heterogeneity / total variability): 99.83%
## H^2 (total variability / sampling variability): 582.81
##
## Test for Heterogeneity:
## Q(df = 6) = 3633.8026, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4330 0.0472 9.1660 <.0001 0.3404 0.5256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5846 -23.1692 -19.1692 -19.5857 -15.1692
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.81%
## H^2 (total variability / sampling variability): 45.72
##
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1045 <.0001 -0.1014 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4638 -14.9277 -6.9277 -9.3825 33.0723
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.97%
## H^2 (unaccounted variability / sampling variability): 32.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 109.9605, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4546, p-val = 0.4832
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0837 0.0253 -3.3138 0.0009 -0.1332 -0.0342
## continentEurope -0.0066 0.0330 -0.1998 0.8416 -0.0712 0.0580
## continentNorth America 0.0316 0.0356 0.8892 0.3739 -0.0381 0.1013
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3843 -18.7685 -12.7685 -13.9402 11.2315
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0341
## I^2 (residual heterogeneity / unaccounted variability): 98.09%
## H^2 (unaccounted variability / sampling variability): 52.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 195.1708, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5208, p-val = 0.4705
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1430 0.0925 -1.5464 0.1220 -0.3243 0.0382
## mean.age 0.0013 0.0018 0.7217 0.4705 -0.0022 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5846 -23.1692 -19.1692 -19.5857 -15.1692
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.81%
## H^2 (total variability / sampling variability): 45.72
##
## Test for Heterogeneity:
## Q(df = 6) = 209.9416, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1045 <.0001 -0.1014 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5819 -23.1638 -19.1638 -19.5803 -15.1638
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.75%
## H^2 (total variability / sampling variability): 44.45
##
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1043 <.0001 -0.1013 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4614 -14.9228 -6.9228 -9.3776 33.0772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0349
## I^2 (residual heterogeneity / unaccounted variability): 96.90%
## H^2 (unaccounted variability / sampling variability): 32.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 104.0509, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.4584, p-val = 0.4823
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0843 0.0252 -3.3417 0.0008 -0.1337 -0.0349
## continentEurope -0.0055 0.0329 -0.1680 0.8666 -0.0701 0.0590
## continentNorth America 0.0324 0.0355 0.9119 0.3618 -0.0372 0.1020
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.3990 -18.7981 -12.7981 -13.9698 11.2019
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0340
## I^2 (residual heterogeneity / unaccounted variability): 98.01%
## H^2 (unaccounted variability / sampling variability): 50.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 176.8991, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5606, p-val = 0.4540
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1451 0.0921 -1.5763 0.1150 -0.3255 0.0353
## mean.age 0.0013 0.0018 0.7487 0.4540 -0.0022 0.0048
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.5819 -23.1638 -19.1638 -19.5803 -15.1638
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0321
## I^2 (total heterogeneity / total variability): 97.75%
## H^2 (total variability / sampling variability): 44.45
##
## Test for Heterogeneity:
## Q(df = 6) = 192.8175, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0767 0.0126 -6.1043 <.0001 -0.1013 -0.0521 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2626 -22.5253 -18.5253 -18.9418 -14.5253
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (total heterogeneity / total variability): 98.07%
## H^2 (total variability / sampling variability): 51.88
##
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0134 -5.8080 <.0001 -0.1037 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6895 -9.3790 -5.3790 -5.7955 -1.3790
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.78
##
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2392 0.0424 -5.6470 <.0001 -0.3222 -0.1562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2889 -14.5777 -6.5777 -9.0325 33.4223
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0367
## I^2 (residual heterogeneity / unaccounted variability): 97.23%
## H^2 (unaccounted variability / sampling variability): 36.10
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 107.9715, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.5427, p-val = 0.4624
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0868 0.0265 -3.2810 0.0010 -0.1387 -0.0350 **
## continentEurope -0.0040 0.0345 -0.1168 0.9070 -0.0717 0.0636
## continentNorth America 0.0363 0.0373 0.9738 0.3302 -0.0368 0.1094
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.1904 -6.3808 1.6192 -0.8356 41.6192
##
## tau^2 (estimated amount of residual heterogeneity): 0.0114 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.1067
## I^2 (residual heterogeneity / unaccounted variability): 96.98%
## H^2 (unaccounted variability / sampling variability): 33.08
## R^2 (amount of heterogeneity accounted for): 3.46%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 178.2441, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.1733, p-val = 0.3373
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3311 0.0768 -4.3112 <.0001 -0.4816 -0.1806
## continentEurope 0.1143 0.1008 1.1338 0.2569 -0.0833 0.3118
## continentNorth America 0.1521 0.1082 1.4057 0.1598 -0.0600 0.3642
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.0671 -18.1342 -12.1342 -13.3059 11.8658
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0368
## I^2 (residual heterogeneity / unaccounted variability): 98.35%
## H^2 (unaccounted variability / sampling variability): 60.47
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 195.2758, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3968, p-val = 0.5288
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1399 0.0996 -1.4050 0.1600 -0.3350 0.0553
## mean.age 0.0012 0.0019 0.6299 0.5288 -0.0026 0.0050
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6041 -7.2083 -1.2083 -2.3800 22.7917
##
## tau^2 (estimated amount of residual heterogeneity): 0.0134 (SE = 0.0090)
## tau (square root of estimated tau^2 value): 0.1156
## I^2 (residual heterogeneity / unaccounted variability): 98.26%
## H^2 (unaccounted variability / sampling variability): 57.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 233.8725, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3336, p-val = 0.5635
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4172 0.3113 -1.3400 0.1802 -1.0274 0.1930
## mean.age 0.0035 0.0060 0.5776 0.5635 -0.0083 0.0153
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.2626 -22.5253 -18.5253 -18.9418 -14.5253
##
## tau^2 (estimated amount of total heterogeneity): 0.0012 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0342
## I^2 (total heterogeneity / total variability): 98.07%
## H^2 (total variability / sampling variability): 51.88
##
## Test for Heterogeneity:
## Q(df = 6) = 205.7391, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0775 0.0134 -5.8080 <.0001 -0.1037 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6895 -9.3790 -5.3790 -5.7955 -1.3790
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.1086
## I^2 (total heterogeneity / total variability): 98.21%
## H^2 (total variability / sampling variability): 55.78
##
## Test for Heterogeneity:
## Q(df = 6) = 284.3511, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2392 0.0424 -5.6470 <.0001 -0.3222 -0.1562 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4637 -20.9274 -16.9274 -17.3438 -12.9274
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (total heterogeneity / total variability): 96.41%
## H^2 (total variability / sampling variability): 27.87
##
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0806 0.0145 -5.5610 <.0001 -0.1091 -0.0522 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6721 -9.3442 -5.3442 -5.7607 -1.3442
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0.1087
## I^2 (total heterogeneity / total variability): 97.61%
## H^2 (total variability / sampling variability): 41.77
##
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2311 0.0425 -5.4373 <.0001 -0.3144 -0.1478 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9104 -27.8209 -23.8209 -24.2373 -19.8209
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 79.78%
## H^2 (total variability / sampling variability): 4.94
##
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0074 0.0088 0.8469 0.3971 -0.0098 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.5136 -13.0272 -5.0272 -7.4820 34.9728
##
## tau^2 (estimated amount of residual heterogeneity): 0.0018 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0422
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability): 23.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 64.1273, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.0897, p-val = 0.5799
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0940 0.0308 -3.0520 0.0023 -0.1544 -0.0336 **
## continentEurope 0.0020 0.0404 0.0496 0.9604 -0.0771 0.0811
## continentNorth America 0.0391 0.0433 0.9028 0.3666 -0.0458 0.1241
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0362 -6.0724 1.9276 -0.5272 41.9276
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0094)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 96.47%
## H^2 (unaccounted variability / sampling variability): 28.30
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 174.4541, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.7403, p-val = 0.4189
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3140 0.0798 -3.9350 <.0001 -0.4704 -0.1576
## continentEurope 0.0975 0.1046 0.9318 0.3515 -0.1075 0.3025
## continentNorth America 0.1455 0.1129 1.2890 0.1974 -0.0757 0.3667
##
## intrcpt ***
## continentEurope
## continentNorth America
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0194
## I^2 (residual heterogeneity / unaccounted variability): 71.00%
## H^2 (unaccounted variability / sampling variability): 3.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 4) = 11.7950, p-val = 0.0189
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9695, p-val = 0.6158
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0188 0.0166 1.1344 0.2566 -0.0137 0.0513
## continentEurope -0.0212 0.0218 -0.9736 0.3302 -0.0640 0.0215
## continentNorth America -0.0095 0.0230 -0.4111 0.6810 -0.0546 0.0357
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.7135 -17.4271 -11.4271 -12.5987 12.5729
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0358
## I^2 (residual heterogeneity / unaccounted variability): 96.36%
## H^2 (unaccounted variability / sampling variability): 27.49
## R^2 (amount of heterogeneity accounted for): 3.07%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 69.9653, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2655, p-val = 0.2606
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1900 0.0984 -1.9317 0.0534 -0.3829 0.0028 .
## mean.age 0.0021 0.0019 1.1249 0.2606 -0.0016 0.0059
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5750 -7.1500 -1.1500 -2.3217 22.8500
##
## tau^2 (estimated amount of residual heterogeneity): 0.0135 (SE = 0.0091)
## tau (square root of estimated tau^2 value): 0.1161
## I^2 (residual heterogeneity / unaccounted variability): 97.94%
## H^2 (unaccounted variability / sampling variability): 48.60
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 243.5127, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2861, p-val = 0.5928
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3982 0.3154 -1.2624 0.2068 -1.0164 0.2200
## mean.age 0.0033 0.0061 0.5348 0.5928 -0.0087 0.0152
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 7; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0144
## I^2 (residual heterogeneity / unaccounted variability): 69.66%
## H^2 (unaccounted variability / sampling variability): 3.30
## R^2 (amount of heterogeneity accounted for): 42.24%
##
## Test for Residual Heterogeneity:
## QE(df = 5) = 18.0249, p-val = 0.0029
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.5823, p-val = 0.0584
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0971 0.0479 2.0287 0.0425 0.0033 0.1909 *
## mean.age -0.0018 0.0009 -1.8927 0.0584 -0.0036 0.0001 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.4637 -20.9274 -16.9274 -17.3438 -12.9274
##
## tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0363
## I^2 (total heterogeneity / total variability): 96.41%
## H^2 (total variability / sampling variability): 27.87
##
## Test for Heterogeneity:
## Q(df = 6) = 92.7450, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0806 0.0145 -5.5610 <.0001 -0.1091 -0.0522 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6721 -9.3442 -5.3442 -5.7607 -1.3442
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0073)
## tau (square root of estimated tau^2 value): 0.1087
## I^2 (total heterogeneity / total variability): 97.61%
## H^2 (total variability / sampling variability): 41.77
##
## Test for Heterogeneity:
## Q(df = 6) = 257.8682, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2311 0.0425 -5.4373 <.0001 -0.3144 -0.1478 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 7; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.9104 -27.8209 -23.8209 -24.2373 -19.8209
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 79.78%
## H^2 (total variability / sampling variability): 4.94
##
## Test for Heterogeneity:
## Q(df = 6) = 32.0022, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0074 0.0088 0.8469 0.3971 -0.0098 0.0246
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2079 -14.4159 -10.4159 -12.2186 1.5841
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (total heterogeneity / total variability): 96.04%
## H^2 (total variability / sampling variability): 25.25
##
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.0110 -7.5977 <.0001 -0.1054 -0.0622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6378 -17.2756 -13.2756 -15.0784 -1.2756
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0133
## I^2 (total heterogeneity / total variability): 97.33%
## H^2 (total variability / sampling variability): 37.51
##
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0017 0.0069 -0.2438 0.8074 -0.0152 0.0118
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.8784 -7.7569 0.2431 -7.7569 40.2431
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1527, p-val = 0.6959
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 63.4417, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0949 0.0039 -24.5103 <.0001 -0.1025 -0.0873
## continentEurope -0.0019 0.0044 -0.4375 0.6617 -0.0104 0.0066
## continentNorth America 0.0434 0.0066 6.5634 <.0001 0.0305 0.0564
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5526 -5.1053 2.8947 -5.1053 42.8947
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0182
## I^2 (residual heterogeneity / unaccounted variability): 93.04%
## H^2 (unaccounted variability / sampling variability): 14.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.3760, p-val = 0.0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.5824, p-val = 0.7474
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0076 0.0133 -0.5729 0.5667 -0.0337 0.0185
## continentEurope 0.0172 0.0225 0.7630 0.4454 -0.0270 0.0614
## continentNorth America 0.0063 0.0227 0.2778 0.7812 -0.0382 0.0508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4296 -8.8591 -2.8591 -6.7797 21.1409
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0259
## I^2 (residual heterogeneity / unaccounted variability): 96.44%
## H^2 (unaccounted variability / sampling variability): 28.06
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 63.5800, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0411, p-val = 0.8393
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1155 0.1571 -0.7353 0.4621 -0.4235 0.1924
## mean.age 0.0007 0.0033 0.2028 0.8393 -0.0058 0.0071
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.3794 -14.7588 -8.7588 -12.6793 15.2412
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0000)
## tau (square root of estimated tau^2 value): 0.0052
## I^2 (residual heterogeneity / unaccounted variability): 78.35%
## H^2 (unaccounted variability / sampling variability): 4.62
## R^2 (amount of heterogeneity accounted for): 84.57%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 9.8418, p-val = 0.0073
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 14.3505, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1404 0.0366 -3.8364 0.0001 -0.2121 -0.0687 ***
## mean.age 0.0030 0.0008 3.7882 0.0002 0.0014 0.0045 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.2079 -14.4159 -10.4159 -12.2186 1.5841
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (total heterogeneity / total variability): 96.04%
## H^2 (total variability / sampling variability): 25.25
##
## Test for Heterogeneity:
## Q(df = 3) = 63.5945, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0838 0.0110 -7.5977 <.0001 -0.1054 -0.0622 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.6378 -17.2756 -13.2756 -15.0784 -1.2756
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0133
## I^2 (total heterogeneity / total variability): 97.33%
## H^2 (total variability / sampling variability): 37.51
##
## Test for Heterogeneity:
## Q(df = 3) = 174.4010, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0017 0.0069 -0.2438 0.8074 -0.0152 0.0118
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9518 -13.9036 -9.9036 -11.7064 2.0964
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0231
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.38
##
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0842 0.0119 -7.0709 <.0001 -0.1075 -0.0608 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8141 -17.6282 -13.6282 -15.4310 -1.6282
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0125
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.37
##
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0019 0.0065 -0.2928 0.7697 -0.0147 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9033 -5.8067 -1.8067 -3.6095 10.1933
##
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value): 0.0900
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.60
##
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2864 0.0458 -6.2497 <.0001 -0.3762 -0.1966 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5250 -7.0500 0.9500 -7.0500 40.9500
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8964, p-val = 0.3437
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 68.8365, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0926 0.0038 -24.3258 <.0001 -0.1000 -0.0851
## continentEurope -0.0039 0.0043 -0.9013 0.3674 -0.0122 0.0045
## continentNorth America 0.0434 0.0066 6.6023 <.0001 0.0305 0.0563
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6318 -5.2637 2.7363 -5.2637 42.7363
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (residual heterogeneity / unaccounted variability): 92.13%
## H^2 (unaccounted variability / sampling variability): 12.70
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.7009, p-val = 0.0004
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.6519, p-val = 0.7218
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0078 0.0123 -0.6330 0.5267 -0.0319 0.0163
## continentEurope 0.0168 0.0208 0.8071 0.4196 -0.0239 0.0574
## continentNorth America 0.0063 0.0209 0.3018 0.7628 -0.0347 0.0473
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1327 -4.2654 3.7346 -4.2654 43.7346
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0202
## I^2 (residual heterogeneity / unaccounted variability): 49.47%
## H^2 (unaccounted variability / sampling variability): 1.98
## R^2 (amount of heterogeneity accounted for): 94.98%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.9790, p-val = 0.1595
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 34.2919, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3235 0.0191 -16.9022 <.0001 -0.3610 -0.2860
## continentEurope -0.0117 0.0287 -0.4071 0.6839 -0.0679 0.0446
## continentNorth America 0.1730 0.0331 5.2319 <.0001 0.1082 0.2378
##
## intrcpt ***
## continentEurope
## continentNorth America ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2380 -8.4760 -2.4760 -6.3965 21.5240
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0285
## I^2 (residual heterogeneity / unaccounted variability): 97.10%
## H^2 (unaccounted variability / sampling variability): 34.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 68.6838, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0132, p-val = 0.9084
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0645 0.1722 -0.3747 0.7079 -0.4020 0.2730
## mean.age -0.0004 0.0036 -0.1150 0.9084 -0.0075 0.0067
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.4026 -14.8053 -8.8053 -12.7259 15.1947
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0000)
## tau (square root of estimated tau^2 value): 0.0051
## I^2 (residual heterogeneity / unaccounted variability): 77.91%
## H^2 (unaccounted variability / sampling variability): 4.53
## R^2 (amount of heterogeneity accounted for): 83.30%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 9.5913, p-val = 0.0083
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.1657, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1321 0.0359 -3.6819 0.0002 -0.2025 -0.0618 ***
## mean.age 0.0028 0.0008 3.6285 0.0003 0.0013 0.0043 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5391 -3.0783 2.9217 -0.9988 26.9217
##
## tau^2 (estimated amount of residual heterogeneity): 0.0122 (SE = 0.0125)
## tau (square root of estimated tau^2 value): 0.1106
## I^2 (residual heterogeneity / unaccounted variability): 98.08%
## H^2 (unaccounted variability / sampling variability): 52.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 2) = 92.6361, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0213, p-val = 0.8839
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1902 0.6615 -0.2875 0.7737 -1.4867 1.1063
## mean.age -0.0020 0.0139 -0.1460 0.8839 -0.0293 0.0252
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 6.9518 -13.9036 -9.9036 -11.7064 2.0964
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0231
## I^2 (total heterogeneity / total variability): 96.71%
## H^2 (total variability / sampling variability): 30.38
##
## Test for Heterogeneity:
## Q(df = 3) = 69.7329, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0842 0.0119 -7.0709 <.0001 -0.1075 -0.0608 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 8.8141 -17.6282 -13.6282 -15.4310 -1.6282
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0125
## I^2 (total heterogeneity / total variability): 97.09%
## H^2 (total variability / sampling variability): 34.37
##
## Test for Heterogeneity:
## Q(df = 3) = 163.4501, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0019 0.0065 -0.2928 0.7697 -0.0147 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 4; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9033 -5.8067 -1.8067 -3.6095 10.1933
##
## tau^2 (estimated amount of total heterogeneity): 0.0081 (SE = 0.0069)
## tau (square root of estimated tau^2 value): 0.0900
## I^2 (total heterogeneity / total variability): 97.85%
## H^2 (total variability / sampling variability): 46.60
##
## Test for Heterogeneity:
## Q(df = 3) = 94.2788, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2864 0.0458 -6.2497 <.0001 -0.3762 -0.1966 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2. Driving risk-taking
Intercept only model
Meta analysis
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5193 -5.0386 -1.0386 -3.6523 10.9614
##
## tau^2 (estimated amount of total heterogeneity): 0.0047 (SE = 0.0047)
## tau (square root of estimated tau^2 value): 0.0683
## I^2 (total heterogeneity / total variability): 98.86%
## H^2 (total variability / sampling variability): 87.97
##
## Test for Heterogeneity:
## Q(df = 2) = 230.9570, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4651 0.0397 11.7072 <.0001 0.3872 0.5430 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9190 -1.8381 4.1619 -1.8381 28.1619
##
## tau^2 (estimated amount of residual heterogeneity): 0.0093 (SE = 0.0132)
## tau (square root of estimated tau^2 value): 0.0963
## I^2 (residual heterogeneity / unaccounted variability): 99.55%
## H^2 (unaccounted variability / sampling variability): 222.84
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 222.8377, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0072, p-val = 0.9326
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4717 0.0969 4.8681 <.0001 0.2818 0.6616 ***
## continentEurope -0.0100 0.1185 -0.0846 0.9326 -0.2423 0.2223
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 0.9234 -1.8468 4.1532 -1.8468 28.1532
##
## tau^2 (estimated amount of residual heterogeneity): 0.0092 (SE = 0.0131)
## tau (square root of estimated tau^2 value): 0.0958
## I^2 (residual heterogeneity / unaccounted variability): 99.46%
## H^2 (unaccounted variability / sampling variability): 184.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 184.1325, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0169, p-val = 0.8966
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5177 0.4093 1.2650 0.2059 -0.2844 1.3199
## mean.age -0.0009 0.0072 -0.1300 0.8966 -0.0151 0.0132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7744 -3.5487 2.4513 -3.5487 26.4513
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0402
## I^2 (residual heterogeneity / unaccounted variability): 96.14%
## H^2 (unaccounted variability / sampling variability): 25.91
## R^2 (amount of heterogeneity accounted for): 65.33%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 25.9141, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6424, p-val = 0.0312
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2037 0.1238 1.6447 0.1000 -0.0390 0.4463
## scale1 0.0271 0.0126 2.1546 0.0312 0.0024 0.0517 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.6356 -9.2713 -5.2713 -7.8850 6.7287
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0229
## I^2 (total heterogeneity / total variability): 90.47%
## H^2 (total variability / sampling variability): 10.50
##
## Test for Heterogeneity:
## Q(df = 2) = 28.1947, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1000 0.0141 -7.0814 <.0001 -0.1277 -0.0723 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1103 -4.2205 1.7795 -4.2205 25.7795
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0287
## I^2 (residual heterogeneity / unaccounted variability): 95.70%
## H^2 (unaccounted variability / sampling variability): 23.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 23.2427, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2290, p-val = 0.6323
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0869 0.0315 -2.7587 0.0058 -0.1487 -0.0252 **
## continentEurope -0.0180 0.0377 -0.4785 0.6323 -0.0920 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2447 -4.4894 1.5106 -4.4894 25.5106
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0009)
## tau (square root of estimated tau^2 value): 0.0247
## I^2 (residual heterogeneity / unaccounted variability): 92.96%
## H^2 (unaccounted variability / sampling variability): 14.20
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.2012, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6280, p-val = 0.4281
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1887 0.1132 -1.6673 0.0954 -0.4105 0.0331 .
## mean.age 0.0016 0.0020 0.7925 0.4281 -0.0024 0.0055
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1912 -4.3825 1.6175 -4.3825 25.6175
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0253
## I^2 (residual heterogeneity / unaccounted variability): 87.39%
## H^2 (unaccounted variability / sampling variability): 7.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.9286, p-val = 0.0049
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5605, p-val = 0.4540
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0410 0.0799 -0.5134 0.6077 -0.1977 0.1156
## scale1 -0.0061 0.0081 -0.7487 0.4540 -0.0220 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4849 -8.9698 -4.9698 -7.5835 7.0302
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0249
## I^2 (total heterogeneity / total variability): 91.76%
## H^2 (total variability / sampling variability): 12.13
##
## Test for Heterogeneity:
## Q(df = 2) = 33.4609, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1003 0.0152 -6.5945 <.0001 -0.1300 -0.0705 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.0044 -4.0088 1.9912 -4.0088 25.9912
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0320
## I^2 (residual heterogeneity / unaccounted variability): 96.45%
## H^2 (unaccounted variability / sampling variability): 28.18
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 28.1843, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1685, p-val = 0.6814
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0880 0.0345 -2.5500 0.0108 -0.1557 -0.0204 *
## continentEurope -0.0170 0.0415 -0.4105 0.6814 -0.0984 0.0643
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1213 -4.2425 1.7575 -4.2425 25.7575
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0282
## I^2 (residual heterogeneity / unaccounted variability): 94.41%
## H^2 (unaccounted variability / sampling variability): 17.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.8824, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4918, p-val = 0.4831
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1882 0.1269 -1.4831 0.1380 -0.4368 0.0605
## mean.age 0.0016 0.0023 0.7013 0.4831 -0.0028 0.0060
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1568 -4.3136 1.6864 -4.3136 25.6864
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0263
## I^2 (residual heterogeneity / unaccounted variability): 88.47%
## H^2 (unaccounted variability / sampling variability): 8.67
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6732, p-val = 0.0032
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6974, p-val = 0.4037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0321 0.0830 -0.3867 0.6990 -0.1948 0.1306
## scale1 -0.0071 0.0084 -0.8351 0.4037 -0.0236 0.0095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.4676 -8.9352 -4.9352 -7.5489 7.0648
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0251
## I^2 (total heterogeneity / total variability): 92.27%
## H^2 (total variability / sampling variability): 12.94
##
## Test for Heterogeneity:
## Q(df = 2) = 35.9401, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1074 0.0153 -7.0185 <.0001 -0.1374 -0.0774 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7846 -5.5692 -1.5692 -4.1829 10.4308
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0560
## I^2 (total heterogeneity / total variability): 88.84%
## H^2 (total variability / sampling variability): 8.96
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3845, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3937 0.0344 -11.4325 <.0001 -0.4612 -0.3262 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9907 -3.9814 2.0186 -3.9814 26.0186
##
## tau^2 (estimated amount of residual heterogeneity): 0.0011 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0325
## I^2 (residual heterogeneity / unaccounted variability): 96.69%
## H^2 (unaccounted variability / sampling variability): 30.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 30.2239, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1623, p-val = 0.6871
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0953 0.0348 -2.7384 0.0062 -0.1636 -0.0271 **
## continentEurope -0.0169 0.0419 -0.4028 0.6871 -0.0991 0.0653
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1383 -2.2766 3.7234 -2.2766 27.7234
##
## tau^2 (estimated amount of residual heterogeneity): 0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value): 0.0751
## I^2 (residual heterogeneity / unaccounted variability): 93.79%
## H^2 (unaccounted variability / sampling variability): 16.11
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.1108, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1979, p-val = 0.6564
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4218 0.0787 -5.3571 <.0001 -0.5761 -0.2675 ***
## continentEurope 0.0427 0.0959 0.4449 0.6564 -0.1453 0.2307
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1055 -4.2109 1.7891 -4.2109 25.7891
##
## tau^2 (estimated amount of residual heterogeneity): 0.0008 (SE = 0.0012)
## tau (square root of estimated tau^2 value): 0.0287
## I^2 (residual heterogeneity / unaccounted variability): 94.81%
## H^2 (unaccounted variability / sampling variability): 19.28
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 19.2772, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4767, p-val = 0.4899
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1951 0.1285 -1.5180 0.1290 -0.4470 0.0568
## mean.age 0.0016 0.0023 0.6905 0.4899 -0.0029 0.0060
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0644 -2.1288 3.8712 -2.1288 27.8712
##
## tau^2 (estimated amount of residual heterogeneity): 0.0065 (SE = 0.0099)
## tau (square root of estimated tau^2 value): 0.0809
## I^2 (residual heterogeneity / unaccounted variability): 93.85%
## H^2 (unaccounted variability / sampling variability): 16.25
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 16.2482, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0441, p-val = 0.8337
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3192 0.3545 -0.9003 0.3679 -1.0139 0.3756
## mean.age -0.0013 0.0062 -0.2100 0.8337 -0.0136 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1519 -4.3038 1.6962 -4.3038 25.6962
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0266
## I^2 (residual heterogeneity / unaccounted variability): 89.30%
## H^2 (unaccounted variability / sampling variability): 9.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.3483, p-val = 0.0022
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7082, p-val = 0.4001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0383 0.0836 -0.4576 0.6472 -0.2021 0.1256
## scale1 -0.0072 0.0085 -0.8415 0.4001 -0.0238 0.0095
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9697 -5.9394 0.0606 -5.9394 24.0606
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.1442, p-val = 0.7041
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 16.2403, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1346 0.0692 -1.9432 0.0520 -0.2703 0.0012 .
## scale1 -0.0268 0.0067 -4.0299 <.0001 -0.0399 -0.0138 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0033 -10.0066 -6.0066 -8.6203 5.9934
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0160
## I^2 (total heterogeneity / total variability): 70.46%
## H^2 (total variability / sampling variability): 3.39
##
## Test for Heterogeneity:
## Q(df = 2) = 7.1299, p-val = 0.0283
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1250 0.0113 -11.0163 <.0001 -0.1472 -0.1027 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8701 -5.7402 -1.7402 -4.3539 10.2598
##
## tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0491
## I^2 (total heterogeneity / total variability): 78.69%
## H^2 (total variability / sampling variability): 4.69
##
## Test for Heterogeneity:
## Q(df = 2) = 10.3844, p-val = 0.0056
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.4037 0.0334 -12.0808 <.0001 -0.4691 -0.3382 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2941 -8.5881 -4.5881 -7.2018 7.4119
##
## tau^2 (estimated amount of total heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0239
## I^2 (total heterogeneity / total variability): 72.71%
## H^2 (total variability / sampling variability): 3.66
##
## Test for Heterogeneity:
## Q(df = 2) = 7.9259, p-val = 0.0190
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0284 0.0165 1.7193 0.0856 -0.0040 0.0608 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6212 -5.2424 0.7576 -5.2424 24.7576
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0155
## I^2 (residual heterogeneity / unaccounted variability): 77.83%
## H^2 (unaccounted variability / sampling variability): 4.51
## R^2 (amount of heterogeneity accounted for): 6.21%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.5101, p-val = 0.0337
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9489, p-val = 0.3300
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1030 0.0253 -4.0791 <.0001 -0.1525 -0.0535 ***
## continentEurope -0.0274 0.0281 -0.9741 0.3300 -0.0825 0.0277
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3515 -2.7030 3.2970 -2.7030 27.2970
##
## tau^2 (estimated amount of residual heterogeneity): 0.0035 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0594
## I^2 (residual heterogeneity / unaccounted variability): 90.07%
## H^2 (unaccounted variability / sampling variability): 10.07
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 10.0712, p-val = 0.0015
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3538, p-val = 0.5520
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4474 0.0823 -5.4365 <.0001 -0.6087 -0.2861 ***
## continentEurope 0.0556 0.0934 0.5948 0.5520 -0.1275 0.2387
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0314
## I^2 (residual heterogeneity / unaccounted variability): 87.21%
## H^2 (unaccounted variability / sampling variability): 7.82
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.8207, p-val = 0.0052
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2075, p-val = 0.6487
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0127 0.0404 0.3130 0.7543 -0.0666 0.0919
## continentEurope 0.0213 0.0469 0.4555 0.6487 -0.0705 0.1132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.9434 -5.8867 0.1133 -5.8867 24.1133
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0086
## I^2 (residual heterogeneity / unaccounted variability): 45.74%
## H^2 (unaccounted variability / sampling variability): 1.84
## R^2 (amount of heterogeneity accounted for): 71.06%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8428, p-val = 0.1746
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7629, p-val = 0.0965
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2445 0.0701 -3.4902 0.0005 -0.3818 -0.1072 ***
## mean.age 0.0022 0.0013 1.6622 0.0965 -0.0004 0.0048 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2226 -2.4451 3.5549 -2.4451 27.5549
##
## tau^2 (estimated amount of residual heterogeneity): 0.0045 (SE = 0.0072)
## tau (square root of estimated tau^2 value): 0.0674
## I^2 (residual heterogeneity / unaccounted variability): 89.59%
## H^2 (unaccounted variability / sampling variability): 9.61
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.6083, p-val = 0.0019
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1012, p-val = 0.7504
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2985 0.3369 -0.8860 0.3756 -0.9589 0.3619
## mean.age -0.0019 0.0061 -0.3182 0.7504 -0.0139 0.0100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0343
## I^2 (residual heterogeneity / unaccounted variability): 86.69%
## H^2 (unaccounted variability / sampling variability): 7.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.5112, p-val = 0.0061
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0422, p-val = 0.8372
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0626 0.1671 0.3747 0.7079 -0.2649 0.3901
## mean.age -0.0006 0.0030 -0.2055 0.8372 -0.0065 0.0053
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1930 -4.3861 1.6139 -4.3861 25.6139
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (residual heterogeneity / unaccounted variability): 70.85%
## H^2 (unaccounted variability / sampling variability): 3.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.4308, p-val = 0.0640
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1168, p-val = 0.7325
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0977 0.0760 -1.2858 0.1985 -0.2467 0.0512
## scale1 -0.0027 0.0078 -0.3418 0.7325 -0.0179 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2410 -4.4819 1.5181 -4.4819 25.5181
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0561, p-val = 0.8127
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 10.3282, p-val = 0.0013
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1888 0.0716 -2.6370 0.0084 -0.3292 -0.0485 **
## scale1 -0.0223 0.0069 -3.2138 0.0013 -0.0359 -0.0087 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0544 -6.1088 -0.1088 -6.1088 23.8912
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0004, p-val = 0.9842
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 7.9255, p-val = 0.0049
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1427 0.0438 3.2562 0.0011 0.0568 0.2286 **
## scale1 -0.0119 0.0042 -2.8152 0.0049 -0.0201 -0.0036 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.3. Financial risk-taking
Intercept only model
Meta analysis
ICC’s results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.3821 -28.7643 -24.7643 -22.8754 -24.0143
##
## tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1105
## I^2 (total heterogeneity / total variability): 99.39%
## H^2 (total variability / sampling variability): 165.12
##
## Test for Heterogeneity:
## Q(df = 19) = 2183.3189, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3620 0.0253 14.3022 <.0001 0.3124 0.4116 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.9834 -23.9668 -13.9668 -10.1039 -7.9668
##
## tau^2 (estimated amount of residual heterogeneity): 0.0125 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.1116
## I^2 (residual heterogeneity / unaccounted variability): 98.96%
## H^2 (unaccounted variability / sampling variability): 95.73
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 2113.1540, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.6844, p-val = 0.4429
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2601 0.1207 2.1562 0.0311 0.0237 0.4966 *
## continentEurope 0.0939 0.1238 0.7584 0.4482 -0.1487 0.3365
## continentNorth America 0.2033 0.1647 1.2340 0.2172 -0.1196 0.5261
## continentOceania 0.2174 0.1644 1.3225 0.1860 -0.1048 0.5395
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 17.5947 -35.1894 -29.1894 -26.5182 -27.4751
##
## tau^2 (estimated amount of residual heterogeneity): 0.0077 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0879
## I^2 (residual heterogeneity / unaccounted variability): 99.00%
## H^2 (unaccounted variability / sampling variability): 100.32
## R^2 (amount of heterogeneity accounted for): 36.79%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1793.1801, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 11.4540, p-val = 0.0007
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.0059 0.1909 5.2705 <.0001 0.6318 1.3800 ***
## mean.age -0.0104 0.0031 -3.3844 0.0007 -0.0165 -0.0044 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
ICC’s results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.7012 -29.4025 -23.4025 -20.7314 -21.6882
##
## tau^2 (estimated amount of residual heterogeneity): 0.0108 (SE = 0.0038)
## tau (square root of estimated tau^2 value): 0.1038
## I^2 (residual heterogeneity / unaccounted variability): 99.06%
## H^2 (unaccounted variability / sampling variability): 106.46
## R^2 (amount of heterogeneity accounted for): 11.73%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 2154.3410, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.4514, p-val = 0.0632
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2622 0.0590 4.4439 <.0001 0.1465 0.3778 ***
## scale2 0.0199 0.0107 1.8578 0.0632 -0.0011 0.0408 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.9609 -61.9217 -57.9217 -56.0328 -57.1717
##
## tau^2 (estimated amount of total heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0436
## I^2 (total heterogeneity / total variability): 97.03%
## H^2 (total variability / sampling variability): 33.68
##
## Test for Heterogeneity:
## Q(df = 19) = 398.5644, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1128 0.0104 -10.8620 <.0001 -0.1332 -0.0925 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.6893 -51.3787 -41.3787 -37.5157 -35.3787
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0446
## I^2 (residual heterogeneity / unaccounted variability): 95.69%
## H^2 (unaccounted variability / sampling variability): 23.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 176.0198, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0780, p-val = 0.5564
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1377 0.0559 -2.4640 0.0137 -0.2473 -0.0282 *
## continentEurope 0.0221 0.0571 0.3869 0.6989 -0.0898 0.1339
## continentNorth America 0.0218 0.0728 0.2999 0.7643 -0.1209 0.1646
## continentOceania 0.0851 0.0716 1.1890 0.2345 -0.0552 0.2254
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.5690 -63.1380 -57.1380 -54.4669 -55.4237
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0369
## I^2 (residual heterogeneity / unaccounted variability): 95.52%
## H^2 (unaccounted variability / sampling variability): 22.32
## R^2 (amount of heterogeneity accounted for): 28.70%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 201.3723, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.7292, p-val = 0.0095
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0981 0.0815 1.2031 0.2289 -0.0617 0.2578
## mean.age -0.0034 0.0013 -2.5941 0.0095 -0.0060 -0.0008 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.2781 -58.5563 -52.5563 -49.8852 -50.8420
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0438
## I^2 (residual heterogeneity / unaccounted variability): 95.98%
## H^2 (unaccounted variability / sampling variability): 24.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 362.4088, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.9895, p-val = 0.3199
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1362 0.0257 -5.3019 <.0001 -0.1866 -0.0859 ***
## scale2 0.0046 0.0046 0.9948 0.3199 -0.0045 0.0136
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.6562 -61.3124 -57.3124 -55.4235 -56.5624
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0447
## I^2 (total heterogeneity / total variability): 96.85%
## H^2 (total variability / sampling variability): 31.72
##
## Test for Heterogeneity:
## Q(df = 19) = 370.8055, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1130 0.0106 -10.6696 <.0001 -0.1337 -0.0922 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.3990 -50.7980 -40.7980 -36.9351 -34.7980
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0459
## I^2 (residual heterogeneity / unaccounted variability): 95.76%
## H^2 (unaccounted variability / sampling variability): 23.56
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 187.4807, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.9953, p-val = 0.5734
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1419 0.0562 -2.5262 0.0115 -0.2520 -0.0318 *
## continentEurope 0.0265 0.0574 0.4612 0.6447 -0.0860 0.1390
## continentNorth America 0.0255 0.0738 0.3450 0.7301 -0.1192 0.1701
## continentOceania 0.0885 0.0726 1.2193 0.2227 -0.0538 0.2307
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.8793 -61.7586 -55.7586 -53.0875 -54.0443
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (residual heterogeneity / unaccounted variability): 95.59%
## H^2 (unaccounted variability / sampling variability): 22.67
## R^2 (amount of heterogeneity accounted for): 23.20%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 212.5169, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.5323, p-val = 0.0187
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0895 0.0864 1.0362 0.3001 -0.0798 0.2588
## mean.age -0.0033 0.0014 -2.3521 0.0187 -0.0060 -0.0005 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 28.9455 -57.8910 -51.8910 -49.2199 -50.1767
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0450
## I^2 (residual heterogeneity / unaccounted variability): 95.88%
## H^2 (unaccounted variability / sampling variability): 24.24
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 349.9235, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8848, p-val = 0.3469
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1356 0.0263 -5.1594 <.0001 -0.1871 -0.0841 ***
## scale2 0.0045 0.0047 0.9407 0.3469 -0.0048 0.0137
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.3718 -62.7436 -58.7436 -56.8547 -57.9936
##
## tau^2 (estimated amount of total heterogeneity): 0.0018 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0429
## I^2 (total heterogeneity / total variability): 96.68%
## H^2 (total variability / sampling variability): 30.09
##
## Test for Heterogeneity:
## Q(df = 19) = 368.9261, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1139 0.0102 -11.1850 <.0001 -0.1339 -0.0940 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 14.5166 -29.0332 -25.0332 -23.1444 -24.2832
##
## tau^2 (estimated amount of total heterogeneity): 0.0120 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.1093
## I^2 (total heterogeneity / total variability): 96.56%
## H^2 (total variability / sampling variability): 29.05
##
## Test for Heterogeneity:
## Q(df = 19) = 1010.6427, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2717 0.0254 -10.6780 <.0001 -0.3216 -0.2218 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.1499 -52.2997 -42.2997 -38.4368 -36.2997
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0436
## I^2 (residual heterogeneity / unaccounted variability): 95.45%
## H^2 (unaccounted variability / sampling variability): 21.98
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 170.5647, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.3348, p-val = 0.5059
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1474 0.0540 -2.7280 0.0064 -0.2533 -0.0415 **
## continentEurope 0.0315 0.0552 0.5714 0.5677 -0.0766 0.1397
## continentNorth America 0.0236 0.0707 0.3340 0.7384 -0.1149 0.1621
## continentOceania 0.0932 0.0694 1.3418 0.1797 -0.0429 0.2293
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.1153 -26.2307 -16.2307 -12.3677 -10.2307
##
## tau^2 (estimated amount of residual heterogeneity): 0.0105 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.1024
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability): 20.25
## R^2 (amount of heterogeneity accounted for): 12.27%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 480.3704, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.1956, p-val = 0.1580
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2941 0.1160 -2.5358 0.0112 -0.5214 -0.0668 *
## continentEurope 0.0186 0.1188 0.1567 0.8755 -0.2143 0.2516
## continentNorth America -0.1007 0.1568 -0.6425 0.5205 -0.4080 0.2065
## continentOceania 0.2219 0.1549 1.4325 0.1520 -0.0817 0.5255
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 31.8571 -63.7141 -57.7141 -55.0430 -55.9999
##
## tau^2 (estimated amount of residual heterogeneity): 0.0014 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0369
## I^2 (residual heterogeneity / unaccounted variability): 95.21%
## H^2 (unaccounted variability / sampling variability): 20.88
## R^2 (amount of heterogeneity accounted for): 25.83%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 219.6926, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3061, p-val = 0.0120
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0906 0.0817 1.1091 0.2674 -0.0695 0.2506
## mean.age -0.0033 0.0013 -2.5112 0.0120 -0.0059 -0.0007 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 13.4274 -26.8548 -20.8548 -18.1836 -19.1405
##
## tau^2 (estimated amount of residual heterogeneity): 0.0124 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.1113
## I^2 (residual heterogeneity / unaccounted variability): 96.23%
## H^2 (unaccounted variability / sampling variability): 26.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 1003.3146, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3335, p-val = 0.5636
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4117 0.2437 -1.6896 0.0911 -0.8892 0.0659 .
## mean.age 0.0023 0.0039 0.5775 0.5636 -0.0054 0.0100
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.4892 -58.9783 -52.9783 -50.3072 -51.2640
##
## tau^2 (estimated amount of residual heterogeneity): 0.0019 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0436
## I^2 (residual heterogeneity / unaccounted variability): 95.72%
## H^2 (unaccounted variability / sampling variability): 23.38
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 342.9145, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6091, p-val = 0.4351
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1322 0.0255 -5.1854 <.0001 -0.1821 -0.0822 ***
## scale2 0.0036 0.0046 0.7805 0.4351 -0.0054 0.0126
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 15.7343 -31.4686 -25.4686 -22.7975 -23.7543
##
## tau^2 (estimated amount of residual heterogeneity): 0.0091 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0956
## I^2 (residual heterogeneity / unaccounted variability): 95.08%
## H^2 (unaccounted variability / sampling variability): 20.34
## R^2 (amount of heterogeneity accounted for): 23.59%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 473.9443, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.8991, p-val = 0.0151
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1477 0.0555 -2.6618 0.0078 -0.2565 -0.0390 **
## scale2 -0.0243 0.0100 -2.4288 0.0151 -0.0440 -0.0047 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.0899 -54.1797 -50.1797 -48.2909 -49.4297
##
## tau^2 (estimated amount of total heterogeneity): 0.0027 (SE = 0.0010)
## tau (square root of estimated tau^2 value): 0.0523
## I^2 (total heterogeneity / total variability): 95.50%
## H^2 (total variability / sampling variability): 22.22
##
## Test for Heterogeneity:
## Q(df = 19) = 276.9773, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1317 0.0127 -10.3514 <.0001 -0.1566 -0.1068 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.6323 -21.2646 -17.2646 -15.3757 -16.5146
##
## tau^2 (estimated amount of total heterogeneity): 0.0152 (SE = 0.0060)
## tau (square root of estimated tau^2 value): 0.1234
## I^2 (total heterogeneity / total variability): 95.06%
## H^2 (total variability / sampling variability): 20.23
##
## Test for Heterogeneity:
## Q(df = 19) = 874.0879, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3173 0.0306 -10.3622 <.0001 -0.3773 -0.2572 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 34.0688 -68.1376 -64.1376 -62.2488 -63.3876
##
## tau^2 (estimated amount of total heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0256
## I^2 (total heterogeneity / total variability): 73.04%
## H^2 (total variability / sampling variability): 3.71
##
## Test for Heterogeneity:
## Q(df = 19) = 56.0213, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0309 0.0080 3.8545 0.0001 0.0152 0.0467 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 22.8412 -45.6825 -35.6825 -31.8195 -29.6825
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0520
## I^2 (residual heterogeneity / unaccounted variability): 93.34%
## H^2 (unaccounted variability / sampling variability): 15.02
## R^2 (amount of heterogeneity accounted for): 1.02%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 111.7893, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.9769, p-val = 0.3952
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1796 0.0701 -2.5603 0.0105 -0.3170 -0.0421 *
## continentEurope 0.0427 0.0715 0.5974 0.5502 -0.0974 0.1828
## continentNorth America 0.0805 0.0899 0.8957 0.3704 -0.0957 0.2568
## continentOceania 0.1222 0.0874 1.3978 0.1622 -0.0491 0.2935
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.9661 -19.9323 -9.9323 -6.0693 -3.9323
##
## tau^2 (estimated amount of residual heterogeneity): 0.0123 (SE = 0.0054)
## tau (square root of estimated tau^2 value): 0.1109
## I^2 (residual heterogeneity / unaccounted variability): 90.91%
## H^2 (unaccounted variability / sampling variability): 11.01
## R^2 (amount of heterogeneity accounted for): 19.25%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 118.4667, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.4533, p-val = 0.1415
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4145 0.1792 -2.3134 0.0207 -0.7658 -0.0633 *
## continentEurope 0.0834 0.1817 0.4590 0.6462 -0.2727 0.4396
## continentNorth America 0.0997 0.2190 0.4552 0.6489 -0.3295 0.5288
## continentOceania 0.3445 0.2108 1.6339 0.1023 -0.0688 0.7578
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.86%
## H^2 (unaccounted variability / sampling variability): 2.17
## R^2 (amount of heterogeneity accounted for): 34.53%
##
## Test for Residual Heterogeneity:
## QE(df = 16) = 32.7135, p-val = 0.0081
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.4100, p-val = 0.0933
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0595 0.0674 0.8833 0.3770 -0.0725 0.1916
## continentEurope -0.0228 0.0678 -0.3365 0.7365 -0.1558 0.1101
## continentNorth America -0.1000 0.0756 -1.3235 0.1857 -0.2482 0.0481
## continentOceania -0.0530 0.0706 -0.7501 0.4532 -0.1914 0.0854
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.6317 -55.2634 -49.2634 -46.5923 -47.5491
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0451
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability): 15.61
## R^2 (amount of heterogeneity accounted for): 25.70%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 155.7586, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 5.9073, p-val = 0.0151
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1131 0.1009 1.1208 0.2624 -0.0846 0.3107
## mean.age -0.0040 0.0016 -2.4305 0.0151 -0.0072 -0.0008 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.5990 -19.1980 -13.1980 -10.5269 -11.4837
##
## tau^2 (estimated amount of residual heterogeneity): 0.0163 (SE = 0.0066)
## tau (square root of estimated tau^2 value): 0.1276
## I^2 (residual heterogeneity / unaccounted variability): 95.06%
## H^2 (unaccounted variability / sampling variability): 20.23
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 810.0881, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1349, p-val = 0.7134
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2137 0.2850 -0.7496 0.4535 -0.7723 0.3450
## mean.age -0.0017 0.0046 -0.3673 0.7134 -0.0108 0.0074
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0267
## I^2 (residual heterogeneity / unaccounted variability): 72.87%
## H^2 (unaccounted variability / sampling variability): 3.69
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 46.3798, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6195, p-val = 0.4312
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0214 0.0672 -0.3185 0.7501 -0.1530 0.1102
## mean.age 0.0009 0.0011 0.7871 0.4312 -0.0013 0.0031
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 25.8677 -51.7355 -45.7355 -43.0644 -44.0212
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0518
## I^2 (residual heterogeneity / unaccounted variability): 93.75%
## H^2 (unaccounted variability / sampling variability): 16.00
## R^2 (amount of heterogeneity accounted for): 1.87%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 253.3383, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5424, p-val = 0.2143
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1669 0.0311 -5.3714 <.0001 -0.2278 -0.1060 ***
## scale2 0.0069 0.0056 1.2419 0.2143 -0.0040 0.0178
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.8119 -19.6239 -13.6239 -10.9528 -11.9096
##
## tau^2 (estimated amount of residual heterogeneity): 0.0153 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.1239
## I^2 (residual heterogeneity / unaccounted variability): 92.97%
## H^2 (unaccounted variability / sampling variability): 14.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 383.0457, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5759, p-val = 0.4479
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2649 0.0756 -3.5055 0.0005 -0.4130 -0.1168 ***
## scale2 -0.0102 0.0134 -0.7589 0.4479 -0.0366 0.0161
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 20; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 32.2041 -64.4082 -58.4082 -55.7370 -56.6939
##
## tau^2 (estimated amount of residual heterogeneity): 0.0007 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0272
## I^2 (residual heterogeneity / unaccounted variability): 68.88%
## H^2 (unaccounted variability / sampling variability): 3.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 55.1806, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.2701, p-val = 0.2597
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0519 0.0202 2.5665 0.0103 0.0123 0.0915 *
## scale2 -0.0038 0.0034 -1.1270 0.2597 -0.0105 0.0028
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.3000 -24.6001 -20.6001 -19.6303 -19.2668
##
## tau^2 (estimated amount of total heterogeneity): 0.0036 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0597
## I^2 (total heterogeneity / total variability): 96.50%
## H^2 (total variability / sampling variability): 28.59
##
## Test for Heterogeneity:
## Q(df = 12) = 75.0873, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1042 0.0208 -5.0134 <.0001 -0.1449 -0.0635 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 29.6130 -59.2261 -55.2261 -54.2562 -53.8927
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (total heterogeneity / total variability): 91.39%
## H^2 (total variability / sampling variability): 11.61
##
## Test for Heterogeneity:
## Q(df = 12) = 269.2438, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0012 0.0057 0.2132 0.8312 -0.0100 0.0125
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.7514 -21.5027 -15.5027 -14.3091 -12.0742
##
## tau^2 (estimated amount of residual heterogeneity): 0.0042 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0648
## I^2 (residual heterogeneity / unaccounted variability): 91.65%
## H^2 (unaccounted variability / sampling variability): 11.97
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 50.7173, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5397, p-val = 0.4625
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0605 0.0648 -0.9337 0.3505 -0.1876 0.0665
## continentEurope -0.0506 0.0689 -0.7347 0.4625 -0.1858 0.0845
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.5310 -55.0620 -49.0620 -47.8683 -45.6334
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0145
## I^2 (residual heterogeneity / unaccounted variability): 75.57%
## H^2 (unaccounted variability / sampling variability): 4.09
## R^2 (amount of heterogeneity accounted for): 24.16%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 31.1098, p-val = 0.0011
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.6416, p-val = 0.1041
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0209 0.0146 -1.4334 0.1517 -0.0495 0.0077
## continentEurope 0.0254 0.0156 1.6253 0.1041 -0.0052 0.0560
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.4073 -22.8146 -16.8146 -15.6209 -13.3860
##
## tau^2 (estimated amount of residual heterogeneity): 0.0031 (SE = 0.0021)
## tau (square root of estimated tau^2 value): 0.0556
## I^2 (residual heterogeneity / unaccounted variability): 95.14%
## H^2 (unaccounted variability / sampling variability): 20.56
## R^2 (amount of heterogeneity accounted for): 13.47%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 59.2910, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9591, p-val = 0.1616
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1336 0.1703 0.7844 0.4328 -0.2002 0.4674
## mean.age -0.0040 0.0028 -1.3997 0.1616 -0.0095 0.0016
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 26.5755 -53.1511 -47.1511 -45.9574 -43.7225
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0179
## I^2 (residual heterogeneity / unaccounted variability): 91.31%
## H^2 (unaccounted variability / sampling variability): 11.51
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 212.8375, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0201, p-val = 0.8873
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0064 0.0538 -0.1181 0.9060 -0.1118 0.0991
## mean.age 0.0001 0.0009 0.1417 0.8873 -0.0016 0.0019
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.7959 -21.5918 -15.5918 -14.3982 -12.1633
##
## tau^2 (estimated amount of residual heterogeneity): 0.0042 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0647
## I^2 (residual heterogeneity / unaccounted variability): 92.05%
## H^2 (unaccounted variability / sampling variability): 12.57
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 58.0105, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5703, p-val = 0.4501
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1671 0.0848 -1.9712 0.0487 -0.3333 -0.0010 *
## scale2 0.0132 0.0175 0.7552 0.4501 -0.0210 0.0474
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.5924 -55.1847 -49.1847 -47.9910 -45.7561
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0144
## I^2 (residual heterogeneity / unaccounted variability): 77.79%
## H^2 (unaccounted variability / sampling variability): 4.50
## R^2 (amount of heterogeneity accounted for): 25.38%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 39.3616, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7049, p-val = 0.1000
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0301 0.0197 -1.5244 0.1274 -0.0687 0.0086
## scale2 0.0066 0.0040 1.6447 0.1000 -0.0013 0.0145
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Quadratic with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 12.2897 -24.5794 -20.5794 -19.6096 -19.2461
##
## tau^2 (estimated amount of total heterogeneity): 0.0040 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0629
## I^2 (total heterogeneity / total variability): 96.95%
## H^2 (total variability / sampling variability): 32.83
##
## Test for Heterogeneity:
## Q(df = 12) = 108.8162, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1187 0.0215 -5.5164 <.0001 -0.1608 -0.0765 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 30.0344 -60.0689 -56.0689 -55.0990 -54.7355
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0162
## I^2 (total heterogeneity / total variability): 91.16%
## H^2 (total variability / sampling variability): 11.31
##
## Test for Heterogeneity:
## Q(df = 12) = 287.9791, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0045 0.0056 0.7999 0.4238 -0.0065 0.0155
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.1888 -18.3776 -14.3776 -13.4078 -13.0443
##
## tau^2 (estimated amount of total heterogeneity): 0.0118 (SE = 0.0052)
## tau (square root of estimated tau^2 value): 0.1084
## I^2 (total heterogeneity / total variability): 96.26%
## H^2 (total variability / sampling variability): 26.77
##
## Test for Heterogeneity:
## Q(df = 12) = 627.3178, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2681 0.0312 -8.5824 <.0001 -0.3294 -0.2069 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.9416 -21.8831 -15.8831 -14.6894 -12.4545
##
## tau^2 (estimated amount of residual heterogeneity): 0.0043 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0.0654
## I^2 (residual heterogeneity / unaccounted variability): 92.12%
## H^2 (unaccounted variability / sampling variability): 12.69
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 61.0752, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8908, p-val = 0.3453
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0612 0.0654 -0.9365 0.3490 -0.1894 0.0669
## continentEurope -0.0656 0.0695 -0.9438 0.3453 -0.2018 0.0706
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 28.4516 -56.9031 -50.9031 -49.7094 -47.4746
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0124
## I^2 (residual heterogeneity / unaccounted variability): 69.92%
## H^2 (unaccounted variability / sampling variability): 3.32
## R^2 (amount of heterogeneity accounted for): 41.35%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 25.3747, p-val = 0.0080
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.6370, p-val = 0.0313
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0208 0.0125 -1.6644 0.0960 -0.0452 0.0037 .
## continentEurope 0.0290 0.0135 2.1534 0.0313 0.0026 0.0553 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.9064 -19.8127 -13.8127 -12.6190 -10.3841
##
## tau^2 (estimated amount of residual heterogeneity): 0.0086 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0927
## I^2 (residual heterogeneity / unaccounted variability): 92.63%
## H^2 (unaccounted variability / sampling variability): 13.58
## R^2 (amount of heterogeneity accounted for): 26.92%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 184.0555, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.8997, p-val = 0.0269
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0700 0.0930 -0.7529 0.4515 -0.2523 0.1123
## continentEurope -0.2151 0.0972 -2.2135 0.0269 -0.4056 -0.0246 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 11.7695 -23.5390 -17.5390 -16.3453 -14.1104
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0021)
## tau (square root of estimated tau^2 value): 0.0548
## I^2 (residual heterogeneity / unaccounted variability): 95.20%
## H^2 (unaccounted variability / sampling variability): 20.82
## R^2 (amount of heterogeneity accounted for): 24.01%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 80.5192, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8400, p-val = 0.0919
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1652 0.1681 0.9828 0.3257 -0.1642 0.4945
## mean.age -0.0047 0.0028 -1.6852 0.0919 -0.0102 0.0008 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.0700 -54.1399 -48.1399 -46.9462 -44.7114
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0171
## I^2 (residual heterogeneity / unaccounted variability): 90.82%
## H^2 (unaccounted variability / sampling variability): 10.89
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 216.8083, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2241, p-val = 0.6360
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0197 0.0516 -0.3820 0.7024 -0.1209 0.0814
## mean.age 0.0004 0.0008 0.4733 0.6360 -0.0013 0.0021
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 7.9280 -15.8560 -9.8560 -8.6623 -6.4275
##
## tau^2 (estimated amount of residual heterogeneity): 0.0129 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.1138
## I^2 (residual heterogeneity / unaccounted variability): 95.78%
## H^2 (unaccounted variability / sampling variability): 23.71
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 515.3742, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0007, p-val = 0.9787
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2771 0.3255 -0.8514 0.3946 -0.9150 0.3608
## mean.age 0.0001 0.0052 0.0267 0.9787 -0.0101 0.0104
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 10.9257 -21.8514 -15.8514 -14.6577 -12.4228
##
## tau^2 (estimated amount of residual heterogeneity): 0.0044 (SE = 0.0028)
## tau (square root of estimated tau^2 value): 0.0665
## I^2 (residual heterogeneity / unaccounted variability): 92.75%
## H^2 (unaccounted variability / sampling variability): 13.79
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 73.6877, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7831, p-val = 0.3762
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1937 0.0866 -2.2361 0.0253 -0.3634 -0.0239 *
## scale2 0.0158 0.0179 0.8849 0.3762 -0.0192 0.0509
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age\({ }^{2}\) effect
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 27.6087 -55.2174 -49.2174 -48.0238 -45.7889
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0150
## I^2 (residual heterogeneity / unaccounted variability): 79.59%
## H^2 (unaccounted variability / sampling variability): 4.90
## R^2 (amount of heterogeneity accounted for): 14.35%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 47.3630, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.6255, p-val = 0.2023
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0206 0.0203 -1.0142 0.3105 -0.0604 0.0192
## scale2 0.0053 0.0042 1.2750 0.2023 -0.0028 0.0135
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 13; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 9.1025 -18.2050 -12.2050 -11.0113 -8.7765
##
## tau^2 (estimated amount of residual heterogeneity): 0.0099 (SE = 0.0046)
## tau (square root of estimated tau^2 value): 0.0996
## I^2 (residual heterogeneity / unaccounted variability): 95.17%
## H^2 (unaccounted variability / sampling variability): 20.69
## R^2 (amount of heterogeneity accounted for): 15.60%
##
## Test for Residual Heterogeneity:
## QE(df = 11) = 306.0790, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.7118, p-val = 0.0996
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0744 0.1208 -0.6158 0.5380 -0.3111 0.1624
## scale2 -0.0431 0.0262 -1.6468 0.0996 -0.0943 0.0082 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.4. Recreational risk-taking
Intercept only model
Meta analysis
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2391 -6.4783 -2.4783 -5.0920 9.5217
##
## tau^2 (estimated amount of total heterogeneity): 0.0023 (SE = 0.0023)
## tau (square root of estimated tau^2 value): 0.0476
## I^2 (total heterogeneity / total variability): 98.16%
## H^2 (total variability / sampling variability): 54.25
##
## Test for Heterogeneity:
## Q(df = 2) = 151.9451, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4688 0.0278 16.8697 <.0001 0.4143 0.5232 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2832 -2.5664 3.4336 -2.5664 27.4336
##
## tau^2 (estimated amount of residual heterogeneity): 0.0045 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0668
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 138.41
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 138.4144, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0112, p-val = 0.9157
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4628 0.0675 6.8542 <.0001 0.3304 0.5951 ***
## continentEurope 0.0087 0.0825 0.1058 0.9157 -0.1530 0.1704
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3288 -2.6576 3.3424 -2.6576 27.3424
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0058)
## tau (square root of estimated tau^2 value): 0.0638
## I^2 (residual heterogeneity / unaccounted variability): 99.05%
## H^2 (unaccounted variability / sampling variability): 105.13
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 105.1267, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1096, p-val = 0.7406
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5582 0.2732 2.0435 0.0410 0.0228 1.0937 *
## mean.age -0.0016 0.0048 -0.3311 0.7406 -0.0110 0.0078
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8111 -3.6222 2.3778 -3.6222 26.3778
##
## tau^2 (estimated amount of residual heterogeneity): 0.0015 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (residual heterogeneity / unaccounted variability): 96.61%
## H^2 (unaccounted variability / sampling variability): 29.47
## R^2 (amount of heterogeneity accounted for): 33.24%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 29.4684, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9478, p-val = 0.1628
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3056 0.1193 2.5625 0.0104 0.0719 0.5393 *
## scale1 0.0169 0.0121 1.3956 0.1628 -0.0068 0.0406
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5828 -7.1656 -3.1656 -5.7793 8.8344
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0397
## I^2 (total heterogeneity / total variability): 96.50%
## H^2 (total variability / sampling variability): 28.59
##
## Test for Heterogeneity:
## Q(df = 2) = 79.5694, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1674 0.0235 -7.1081 <.0001 -0.2135 -0.1212 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5967 -3.1933 2.8067 -3.1933 26.8067
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0034)
## tau (square root of estimated tau^2 value): 0.0486
## I^2 (residual heterogeneity / unaccounted variability): 98.46%
## H^2 (unaccounted variability / sampling variability): 64.77
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 64.7722, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3148, p-val = 0.5747
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1436 0.0506 -2.8352 0.0046 -0.2428 -0.0443 **
## continentEurope -0.0344 0.0614 -0.5611 0.5747 -0.1547 0.0858
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7508 -3.5015 2.4985 -3.5015 26.4985
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0414
## I^2 (residual heterogeneity / unaccounted variability): 97.31%
## H^2 (unaccounted variability / sampling variability): 37.18
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 37.1791, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7964, p-val = 0.3722
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3282 0.1820 -1.8036 0.0713 -0.6849 0.0285 .
## mean.age 0.0029 0.0032 0.8924 0.3722 -0.0034 0.0092
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6048 -3.2096 2.7904 -3.2096 26.7904
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0033)
## tau (square root of estimated tau^2 value): 0.0475
## I^2 (residual heterogeneity / unaccounted variability): 95.49%
## H^2 (unaccounted variability / sampling variability): 22.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 22.1883, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3765, p-val = 0.5395
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0795 0.1454 -0.5469 0.5845 -0.3645 0.2055
## scale1 -0.0091 0.0148 -0.6136 0.5395 -0.0381 0.0199
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.4996 -6.9992 -2.9992 -5.6129 9.0008
##
## tau^2 (estimated amount of total heterogeneity): 0.0017 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0415
## I^2 (total heterogeneity / total variability): 96.82%
## H^2 (total variability / sampling variability): 31.41
##
## Test for Heterogeneity:
## Q(df = 2) = 91.5192, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1648 0.0246 -6.7105 <.0001 -0.2130 -0.1167 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5080 -3.0160 2.9840 -3.0160 26.9840
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability): 78.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 77.9964, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1987, p-val = 0.6558
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1443 0.0550 -2.6214 0.0088 -0.2522 -0.0364 **
## continentEurope -0.0298 0.0668 -0.4457 0.6558 -0.1607 0.1012
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6332 -3.2665 2.7335 -3.2665 26.7335
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0468
## I^2 (residual heterogeneity / unaccounted variability): 97.89%
## H^2 (unaccounted variability / sampling variability): 47.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 47.4250, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5459, p-val = 0.4600
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3139 0.2039 -1.5397 0.1236 -0.7136 0.0857
## mean.age 0.0027 0.0036 0.7388 0.4600 -0.0044 0.0097
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6255 -3.2510 2.7490 -3.2510 26.7490
##
## tau^2 (estimated amount of residual heterogeneity): 0.0022 (SE = 0.0032)
## tau (square root of estimated tau^2 value): 0.0465
## I^2 (residual heterogeneity / unaccounted variability): 95.33%
## H^2 (unaccounted variability / sampling variability): 21.43
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 21.4300, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5626, p-val = 0.4532
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0599 0.1424 -0.4206 0.6741 -0.3389 0.2192
## scale1 -0.0109 0.0145 -0.7501 0.4532 -0.0392 0.0175
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6536 -7.3073 -3.3073 -5.9210 8.6927
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0384
## I^2 (total heterogeneity / total variability): 96.43%
## H^2 (total variability / sampling variability): 28.02
##
## Test for Heterogeneity:
## Q(df = 2) = 82.9062, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1703 0.0228 -7.4715 <.0001 -0.2149 -0.1256 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7562 -5.5124 -1.5124 -4.1261 10.4876
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0556
## I^2 (total heterogeneity / total variability): 88.03%
## H^2 (total variability / sampling variability): 8.35
##
## Test for Heterogeneity:
## Q(df = 2) = 12.5156, p-val = 0.0019
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3731 0.0344 -10.8491 <.0001 -0.4404 -0.3057 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5614 -3.1228 2.8772 -3.1228 26.8772
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0504
## I^2 (residual heterogeneity / unaccounted variability): 98.61%
## H^2 (unaccounted variability / sampling variability): 72.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 72.1466, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1397, p-val = 0.7086
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1538 0.0522 -2.9450 0.0032 -0.2562 -0.0515 **
## continentEurope -0.0237 0.0634 -0.3738 0.7086 -0.1479 0.1005
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1419 -4.2838 1.7162 -4.2838 25.7162
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0207
## I^2 (residual heterogeneity / unaccounted variability): 53.32%
## H^2 (unaccounted variability / sampling variability): 2.14
## R^2 (amount of heterogeneity accounted for): 86.06%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.1424, p-val = 0.1433
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 6.3646, p-val = 0.0116
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4421 0.0331 -13.3624 <.0001 -0.5070 -0.3773 ***
## continentEurope 0.0967 0.0383 2.5228 0.0116 0.0216 0.1718 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6692 -3.3384 2.6616 -3.3384 26.6616
##
## tau^2 (estimated amount of residual heterogeneity): 0.0020 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0451
## I^2 (residual heterogeneity / unaccounted variability): 97.81%
## H^2 (unaccounted variability / sampling variability): 45.58
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 45.5752, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4202, p-val = 0.5168
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2964 0.1967 -1.5069 0.1318 -0.6818 0.0891
## mean.age 0.0023 0.0035 0.6482 0.5168 -0.0046 0.0091
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6730 -3.3461 2.6539 -3.3461 26.6539
##
## tau^2 (estimated amount of residual heterogeneity): 0.0016 (SE = 0.0029)
## tau (square root of estimated tau^2 value): 0.0403
## I^2 (residual heterogeneity / unaccounted variability): 78.80%
## H^2 (unaccounted variability / sampling variability): 4.72
## R^2 (amount of heterogeneity accounted for): 47.39%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.7169, p-val = 0.0299
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.4489, p-val = 0.1176
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0721 0.1937 -0.3723 0.7096 -0.4517 0.3075
## mean.age -0.0054 0.0034 -1.5649 0.1176 -0.0121 0.0014
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7531 -3.5062 2.4938 -3.5062 26.4938
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0025)
## tau (square root of estimated tau^2 value): 0.0407
## I^2 (residual heterogeneity / unaccounted variability): 94.33%
## H^2 (unaccounted variability / sampling variability): 17.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 17.6441, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7341, p-val = 0.3915
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0650 0.1251 -0.5191 0.6037 -0.3102 0.1803
## scale1 -0.0109 0.0127 -0.8568 0.3915 -0.0359 0.0140
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4286 -2.8572 3.1428 -2.8572 27.1428
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0048)
## tau (square root of estimated tau^2 value): 0.0543
## I^2 (residual heterogeneity / unaccounted variability): 87.74%
## H^2 (unaccounted variability / sampling variability): 8.16
## R^2 (amount of heterogeneity accounted for): 4.49%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.1573, p-val = 0.0043
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1699, p-val = 0.2794
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1832 0.1787 -1.0256 0.3051 -0.5334 0.1669
## scale1 -0.0195 0.0181 -1.0816 0.2794 -0.0549 0.0159
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.3138 -6.6277 -2.6277 -5.2414 9.3723
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0443
## I^2 (total heterogeneity / total variability): 94.60%
## H^2 (total variability / sampling variability): 18.51
##
## Test for Heterogeneity:
## Q(df = 2) = 42.6115, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1838 0.0268 -6.8594 <.0001 -0.2363 -0.1313 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.2999 -8.5998 -4.5998 -7.2135 7.4002
##
## tau^2 (estimated amount of total heterogeneity): 0 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0
## I^2 (total heterogeneity / total variability): 0.00%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 1.5405, p-val = 0.4629
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.3629 0.0111 -32.5617 <.0001 -0.3847 -0.3410 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 4.0570 -8.1139 -4.1139 -6.7276 7.8861
##
## tau^2 (estimated amount of total heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0190
## I^2 (total heterogeneity / total variability): 62.05%
## H^2 (total variability / sampling variability): 2.64
##
## Test for Heterogeneity:
## Q(df = 2) = 4.6848, p-val = 0.0961
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0331 0.0142 2.3373 0.0194 0.0053 0.0608 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6478 -3.2956 2.7044 -3.2956 26.7044
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0458
## I^2 (residual heterogeneity / unaccounted variability): 96.80%
## H^2 (unaccounted variability / sampling variability): 31.22
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 31.2172, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8513, p-val = 0.3562
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1444 0.0507 -2.8492 0.0044 -0.2438 -0.0451 **
## continentEurope -0.0558 0.0604 -0.9226 0.3562 -0.1742 0.0627
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.5625 -5.1250 0.8750 -5.1250 24.8750
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.8746, p-val = 0.3497
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6659, p-val = 0.4145
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4115 0.0606 -6.7919 <.0001 -0.5302 -0.2927 ***
## continentEurope 0.0503 0.0616 0.8160 0.4145 -0.0705 0.1711
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9403, p-val = 0.3322
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 3.7445, p-val = 0.0530
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0156 0.0279 -0.5595 0.5758 -0.0702 0.0390
## continentEurope 0.0554 0.0286 1.9351 0.0530 -0.0007 0.1115 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9055 -3.8110 2.1890 -3.8110 26.1890
##
## tau^2 (estimated amount of residual heterogeneity): 0.0012 (SE = 0.0018)
## tau (square root of estimated tau^2 value): 0.0347
## I^2 (residual heterogeneity / unaccounted variability): 92.95%
## H^2 (unaccounted variability / sampling variability): 14.18
## R^2 (amount of heterogeneity accounted for): 38.78%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.1799, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0710, p-val = 0.1501
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4170 0.1626 -2.5654 0.0103 -0.7357 -0.0984 *
## mean.age 0.0042 0.0029 1.4391 0.1501 -0.0015 0.0099
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.1499 -4.2999 1.7001 -4.2999 25.7001
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0011)
## tau (square root of estimated tau^2 value): 0.0157
## I^2 (residual heterogeneity / unaccounted variability): 31.02%
## H^2 (unaccounted variability / sampling variability): 1.45
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4497, p-val = 0.2286
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2150, p-val = 0.6429
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2770 0.1833 -1.5113 0.1307 -0.6363 0.0822
## mean.age -0.0016 0.0036 -0.4637 0.6429 -0.0086 0.0053
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0171
## I^2 (residual heterogeneity / unaccounted variability): 61.44%
## H^2 (unaccounted variability / sampling variability): 2.59
## R^2 (amount of heterogeneity accounted for): 19.04%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5933, p-val = 0.1073
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.0246, p-val = 0.1548
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1919 0.1119 1.7139 0.0865 -0.0275 0.4112 .
## mean.age -0.0030 0.0021 -1.4229 0.1548 -0.0071 0.0011
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3573 -2.7146 3.2854 -2.7146 27.2854
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0602
## I^2 (residual heterogeneity / unaccounted variability): 93.59%
## H^2 (unaccounted variability / sampling variability): 15.59
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 15.5935, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1044, p-val = 0.7466
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1240 0.1849 -0.6706 0.5025 -0.4865 0.2385
## scale1 -0.0061 0.0188 -0.3231 0.7466 -0.0430 0.0308
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9466 -3.8933 2.1067 -3.8933 26.1067
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0027)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.5265, p-val = 0.4681
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0140, p-val = 0.3139
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2910 0.0723 -4.0269 <.0001 -0.4326 -0.1494 ***
## scale1 -0.0070 0.0070 -1.0070 0.3139 -0.0207 0.0067
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.8771 -3.7542 2.2458 -3.7542 26.2458
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0019)
## tau (square root of estimated tau^2 value): 0.0309
## I^2 (residual heterogeneity / unaccounted variability): 69.74%
## H^2 (unaccounted variability / sampling variability): 3.31
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 3.3052, p-val = 0.0691
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6957, p-val = 0.4042
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1155 0.1046 1.1045 0.2694 -0.0895 0.3206
## scale1 -0.0089 0.0107 -0.8341 0.4042 -0.0299 0.0120
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.5. Occupational risk-taking
Intercept only model
Meta analysis
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7913 -5.5827 -1.5827 -4.1964 10.4173
##
## tau^2 (estimated amount of total heterogeneity): 0.0035 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0594
## I^2 (total heterogeneity / total variability): 98.37%
## H^2 (total variability / sampling variability): 61.20
##
## Test for Heterogeneity:
## Q(df = 2) = 143.2600, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4097 0.0347 11.8206 <.0001 0.3417 0.4776 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1260 -2.2520 3.7480 -2.2520 27.7480
##
## tau^2 (estimated amount of residual heterogeneity): 0.0061 (SE = 0.0087)
## tau (square root of estimated tau^2 value): 0.0782
## I^2 (residual heterogeneity / unaccounted variability): 99.29%
## H^2 (unaccounted variability / sampling variability): 141.21
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 141.2076, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1596, p-val = 0.6895
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4355 0.0790 5.5138 <.0001 0.2807 0.5903 ***
## continentEurope -0.0386 0.0965 -0.3995 0.6895 -0.2278 0.1506
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.0653 -2.1306 3.8694 -2.1306 27.8694
##
## tau^2 (estimated amount of residual heterogeneity): 0.0069 (SE = 0.0098)
## tau (square root of estimated tau^2 value): 0.0831
## I^2 (residual heterogeneity / unaccounted variability): 99.28%
## H^2 (unaccounted variability / sampling variability): 138.93
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 138.9308, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0282, p-val = 0.8666
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3505 0.3555 0.9860 0.3241 -0.3462 1.0472
## mean.age 0.0011 0.0063 0.1680 0.8666 -0.0112 0.0133
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0155 -6.0310 -0.0310 -6.0310 23.9690
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0081
## I^2 (residual heterogeneity / unaccounted variability): 46.85%
## H^2 (unaccounted variability / sampling variability): 1.88
## R^2 (amount of heterogeneity accounted for): 98.13%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.8815, p-val = 0.1702
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 58.9630, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1567 0.0339 4.6265 <.0001 0.0903 0.2231 ***
## scale1 0.0264 0.0034 7.6787 <.0001 0.0196 0.0331 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.0642 -10.1284 -6.1284 -8.7421 5.8716
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0180
## I^2 (total heterogeneity / total variability): 83.94%
## H^2 (total variability / sampling variability): 6.23
##
## Test for Heterogeneity:
## Q(df = 2) = 16.3123, p-val = 0.0003
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1747 0.0117 -14.9541 <.0001 -0.1976 -0.1518 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.2959 -4.5917 1.4083 -4.5917 25.4083
##
## tau^2 (estimated amount of residual heterogeneity): 0.0006 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0235
## I^2 (residual heterogeneity / unaccounted variability): 93.33%
## H^2 (unaccounted variability / sampling variability): 14.99
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.9881, p-val = 0.0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0666, p-val = 0.7964
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1680 0.0279 -6.0127 <.0001 -0.2228 -0.1133 ***
## continentEurope -0.0085 0.0328 -0.2581 0.7964 -0.0728 0.0559
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3771 -4.7541 1.2459 -4.7541 25.2459
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0213
## I^2 (residual heterogeneity / unaccounted variability): 89.95%
## H^2 (unaccounted variability / sampling variability): 9.95
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.9494, p-val = 0.0016
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2827, p-val = 0.5950
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2283 0.1024 -2.2301 0.0257 -0.4290 -0.0277 *
## mean.age 0.0010 0.0018 0.5317 0.5950 -0.0026 0.0046
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6155 -5.2309 0.7691 -5.2309 24.7691
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0138
## I^2 (residual heterogeneity / unaccounted variability): 61.08%
## H^2 (unaccounted variability / sampling variability): 2.57
## R^2 (amount of heterogeneity accounted for): 41.09%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.5691, p-val = 0.1090
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.7407, p-val = 0.1870
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1129 0.0484 -2.3303 0.0198 -0.2078 -0.0179 *
## scale1 -0.0065 0.0050 -1.3194 0.1870 -0.0162 0.0032
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.1853 -10.3707 -6.3707 -8.9844 5.6293
##
## tau^2 (estimated amount of total heterogeneity): 0.0003 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0167
## I^2 (total heterogeneity / total variability): 81.87%
## H^2 (total variability / sampling variability): 5.52
##
## Test for Heterogeneity:
## Q(df = 2) = 14.2366, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1747 0.0110 -15.8989 <.0001 -0.1963 -0.1532 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3642 -4.7284 1.2716 -4.7284 25.2716
##
## tau^2 (estimated amount of residual heterogeneity): 0.0005 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0219
## I^2 (residual heterogeneity / unaccounted variability): 92.36%
## H^2 (unaccounted variability / sampling variability): 13.08
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.0828, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0655, p-val = 0.7980
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1684 0.0265 -6.3510 <.0001 -0.2203 -0.1164 ***
## continentEurope -0.0079 0.0310 -0.2559 0.7980 -0.0687 0.0528
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4448 -4.8897 1.1103 -4.8897 25.1103
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0197
## I^2 (residual heterogeneity / unaccounted variability): 88.52%
## H^2 (unaccounted variability / sampling variability): 8.71
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.7102, p-val = 0.0032
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2838, p-val = 0.5942
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2253 0.0964 -2.3363 0.0195 -0.4143 -0.0363 *
## mean.age 0.0009 0.0017 0.5328 0.5942 -0.0025 0.0043
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6867 -5.3734 0.6266 -5.3734 24.6266
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0004)
## tau (square root of estimated tau^2 value): 0.0123
## I^2 (residual heterogeneity / unaccounted variability): 55.59%
## H^2 (unaccounted variability / sampling variability): 2.25
## R^2 (amount of heterogeneity accounted for): 46.03%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2516, p-val = 0.1335
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8955, p-val = 0.1686
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1158 0.0444 -2.6084 0.0091 -0.2027 -0.0288 **
## scale1 -0.0062 0.0045 -1.3768 0.1686 -0.0151 0.0026
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.3576 -10.7151 -6.7151 -9.3288 5.2849
##
## tau^2 (estimated amount of total heterogeneity): 0.0002 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0151
## I^2 (total heterogeneity / total variability): 79.07%
## H^2 (total variability / sampling variability): 4.78
##
## Test for Heterogeneity:
## Q(df = 2) = 12.0238, p-val = 0.0024
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1793 0.0101 -17.7267 <.0001 -0.1992 -0.1595 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.7595 -5.5189 -1.5189 -4.1326 10.4811
##
## tau^2 (estimated amount of total heterogeneity): 0.0031 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0560
## I^2 (total heterogeneity / total variability): 87.81%
## H^2 (total variability / sampling variability): 8.21
##
## Test for Heterogeneity:
## Q(df = 2) = 14.3779, p-val = 0.0008
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2714 0.0347 -7.8218 <.0001 -0.3394 -0.2034 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4320 -4.8639 1.1361 -4.8639 25.1361
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0203
## I^2 (residual heterogeneity / unaccounted variability): 91.41%
## H^2 (unaccounted variability / sampling variability): 11.65
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 11.6481, p-val = 0.0006
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0018, p-val = 0.9660
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1780 0.0251 -7.0862 <.0001 -0.2272 -0.1287 ***
## continentEurope -0.0012 0.0293 -0.0427 0.9660 -0.0586 0.0561
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2818 -2.5636 3.4364 -2.5636 27.4364
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0644
## I^2 (residual heterogeneity / unaccounted variability): 91.90%
## H^2 (unaccounted variability / sampling variability): 12.35
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.3468, p-val = 0.0004
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6069, p-val = 0.4360
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3165 0.0700 -4.5214 <.0001 -0.4536 -0.1793 ***
## continentEurope 0.0659 0.0846 0.7790 0.4360 -0.0999 0.2316
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4590 -4.9179 1.0821 -4.9179 25.0821
##
## tau^2 (estimated amount of residual heterogeneity): 0.0004 (SE = 0.0006)
## tau (square root of estimated tau^2 value): 0.0195
## I^2 (residual heterogeneity / unaccounted variability): 88.42%
## H^2 (unaccounted variability / sampling variability): 8.64
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.6391, p-val = 0.0033
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0849, p-val = 0.7708
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2064 0.0950 -2.1725 0.0298 -0.3926 -0.0202 *
## mean.age 0.0005 0.0017 0.2913 0.7708 -0.0029 0.0039
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.1402 -2.2803 3.7197 -2.2803 27.7197
##
## tau^2 (estimated amount of residual heterogeneity): 0.0056 (SE = 0.0085)
## tau (square root of estimated tau^2 value): 0.0746
## I^2 (residual heterogeneity / unaccounted variability): 92.92%
## H^2 (unaccounted variability / sampling variability): 14.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 14.1244, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2355, p-val = 0.6275
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1120 0.3313 -0.3381 0.7353 -0.7613 0.5373
## mean.age -0.0028 0.0059 -0.4853 0.6275 -0.0143 0.0086
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0928 -6.1857 -0.1857 -6.1857 23.8143
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0019
## I^2 (residual heterogeneity / unaccounted variability): 3.10%
## H^2 (unaccounted variability / sampling variability): 1.03
## R^2 (amount of heterogeneity accounted for): 98.36%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0320, p-val = 0.3097
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 9.9312, p-val = 0.0016
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1130 0.0234 -4.8226 <.0001 -0.1590 -0.0671 ***
## scale1 -0.0072 0.0023 -3.1514 0.0016 -0.0117 -0.0027 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.6951 -5.3901 0.6099 -5.3901 24.6099
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4535, p-val = 0.5007
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 13.9245, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0283 0.0684 -0.4139 0.6790 -0.1623 0.1057
## scale1 -0.0247 0.0066 -3.7316 0.0002 -0.0376 -0.0117 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 5.7723 -11.5446 -7.5446 -10.1583 4.4554
##
## tau^2 (estimated amount of total heterogeneity): 0.0000 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0.0005
## I^2 (total heterogeneity / total variability): 0.21%
## H^2 (total variability / sampling variability): 1.00
##
## Test for Heterogeneity:
## Q(df = 2) = 2.1896, p-val = 0.3346
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.1834 0.0050 -36.7338 <.0001 -0.1932 -0.1736 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8349 -5.6697 -1.6697 -4.2834 10.3303
##
## tau^2 (estimated amount of total heterogeneity): 0.0028 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0533
## I^2 (total heterogeneity / total variability): 81.59%
## H^2 (total variability / sampling variability): 5.43
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8097, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2494 0.0360 -6.9232 <.0001 -0.3200 -0.1788 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6823 -7.3645 -3.3645 -5.9782 8.6355
##
## tau^2 (estimated amount of total heterogeneity): 0.0010 (SE = 0.0014)
## tau (square root of estimated tau^2 value): 0.0319
## I^2 (total heterogeneity / total variability): 80.61%
## H^2 (total variability / sampling variability): 5.16
##
## Test for Heterogeneity:
## Q(df = 2) = 9.3161, p-val = 0.0095
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0030 0.0211 -0.1422 0.8869 -0.0444 0.0384
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6864 -7.3729 -1.3729 -7.3729 22.6271
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.3067, p-val = 0.5797
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.8829, p-val = 0.1700
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1525 0.0231 -6.5998 <.0001 -0.1978 -0.1072 ***
## continentEurope -0.0325 0.0237 -1.3722 0.1700 -0.0788 0.0139
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2385 -2.4770 3.5230 -2.4770 27.5230
##
## tau^2 (estimated amount of residual heterogeneity): 0.0046 (SE = 0.0070)
## tau (square root of estimated tau^2 value): 0.0675
## I^2 (residual heterogeneity / unaccounted variability): 92.53%
## H^2 (unaccounted variability / sampling variability): 13.39
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.3916, p-val = 0.0003
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0275, p-val = 0.8683
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2344 0.0926 -2.5302 0.0114 -0.4160 -0.0528 *
## continentEurope -0.0174 0.1051 -0.1659 0.8683 -0.2233 0.1885
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0322
## I^2 (residual heterogeneity / unaccounted variability): 86.94%
## H^2 (unaccounted variability / sampling variability): 7.66
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 7.6565, p-val = 0.0057
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.0764, p-val = 0.2995
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0430 0.0440 -0.9770 0.3286 -0.1293 0.0433
## continentEurope 0.0522 0.0503 1.0375 0.2995 -0.0464 0.1508
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7003 -7.4006 -1.4006 -7.4006 22.5994
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0044, p-val = 0.9471
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.1852, p-val = 0.1393
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2779 0.0641 -4.3366 <.0001 -0.4034 -0.1523 ***
## mean.age 0.0019 0.0013 1.4782 0.1393 -0.0006 0.0043
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3012 -2.6023 3.3977 -2.6023 27.3977
##
## tau^2 (estimated amount of residual heterogeneity): 0.0038 (SE = 0.0061)
## tau (square root of estimated tau^2 value): 0.0618
## I^2 (residual heterogeneity / unaccounted variability): 87.94%
## H^2 (unaccounted variability / sampling variability): 8.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 8.2948, p-val = 0.0040
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2076, p-val = 0.6487
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3963 0.3271 -1.2117 0.2256 -1.0373 0.2447
## mean.age 0.0027 0.0060 0.4556 0.6487 -0.0090 0.0144
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0017 (SE = 0.0026)
## tau (square root of estimated tau^2 value): 0.0407
## I^2 (residual heterogeneity / unaccounted variability): 89.24%
## H^2 (unaccounted variability / sampling variability): 9.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 9.2935, p-val = 0.0023
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.4640, p-val = 0.4957
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1293 0.1974 0.6552 0.5123 -0.2575 0.5162
## mean.age -0.0024 0.0036 -0.6812 0.4957 -0.0094 0.0045
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3089 -4.6179 1.3821 -4.6179 25.3821
##
## tau^2 (estimated amount of residual heterogeneity): 0.0003 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0172
## I^2 (residual heterogeneity / unaccounted variability): 50.92%
## H^2 (unaccounted variability / sampling variability): 2.04
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0375, p-val = 0.1535
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0134, p-val = 0.9080
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1849 0.0617 -2.9980 0.0027 -0.3058 -0.0640 **
## scale1 0.0007 0.0063 0.1156 0.9080 -0.0117 0.0132
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6602 -3.3204 2.6796 -3.3204 26.6796
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0030)
## tau (square root of estimated tau^2 value): 0.0037
## I^2 (residual heterogeneity / unaccounted variability): 0.66%
## H^2 (unaccounted variability / sampling variability): 1.01
## R^2 (amount of heterogeneity accounted for): 99.51%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.0066, p-val = 0.3157
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 12.2996, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0317 0.0700 -0.4535 0.6502 -0.1689 0.1054
## scale1 -0.0241 0.0069 -3.5071 0.0005 -0.0375 -0.0106 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.4443 -4.8886 1.1114 -4.8886 25.1114
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.9106, p-val = 0.3400
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.4055, p-val = 0.0037
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1244 0.0453 2.7456 0.0060 0.0356 0.2132 **
## scale1 -0.0127 0.0044 -2.8992 0.0037 -0.0213 -0.0041 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.6. Health risk-taking
Intercept only model
Meta analysis
ICC’s results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2938 -6.5876 -2.5876 -5.2013 9.4124
##
## tau^2 (estimated amount of total heterogeneity): 0.0021 (SE = 0.0022)
## tau (square root of estimated tau^2 value): 0.0459
## I^2 (total heterogeneity / total variability): 97.34%
## H^2 (total variability / sampling variability): 37.57
##
## Test for Heterogeneity:
## Q(df = 2) = 86.4426, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3790 0.0270 14.0430 <.0001 0.3261 0.4319 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4510 -2.9020 3.0980 -2.9020 27.0980
##
## tau^2 (estimated amount of residual heterogeneity): 0.0032 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0564
## I^2 (residual heterogeneity / unaccounted variability): 98.81%
## H^2 (unaccounted variability / sampling variability): 84.20
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 84.1991, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3303, p-val = 0.5655
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4064 0.0579 7.0201 <.0001 0.2930 0.5199 ***
## continentEurope -0.0405 0.0704 -0.5747 0.5655 -0.1785 0.0976
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3544 -2.7089 3.2911 -2.7089 27.2911
##
## tau^2 (estimated amount of residual heterogeneity): 0.0039 (SE = 0.0055)
## tau (square root of estimated tau^2 value): 0.0621
## I^2 (residual heterogeneity / unaccounted variability): 98.80%
## H^2 (unaccounted variability / sampling variability): 83.27
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 83.2654, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1009, p-val = 0.7507
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2949 0.2675 1.1024 0.2703 -0.2294 0.8193
## mean.age 0.0015 0.0047 0.3177 0.7507 -0.0077 0.0107
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
ICC’s results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7004 -7.4008 -1.4008 -7.4008 22.5992
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0001)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.0020, p-val = 0.9647
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 86.4407, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1852 0.0220 8.3995 <.0001 0.1420 0.2284 ***
## scale1 0.0201 0.0022 9.2973 <.0001 0.0158 0.0243 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fixed effect model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6147 -7.2294 -3.2294 -5.8431 8.7706
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0392
## I^2 (total heterogeneity / total variability): 96.96%
## H^2 (total variability / sampling variability): 32.93
##
## Test for Heterogeneity:
## Q(df = 2) = 94.4415, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0841 0.0232 -3.6265 0.0003 -0.1295 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5085 -3.0171 2.9829 -3.0171 26.9829
##
## tau^2 (estimated amount of residual heterogeneity): 0.0028 (SE = 0.0041)
## tau (square root of estimated tau^2 value): 0.0532
## I^2 (residual heterogeneity / unaccounted variability): 98.94%
## H^2 (unaccounted variability / sampling variability): 94.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 94.3332, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0694, p-val = 0.7921
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0960 0.0549 -1.7475 0.0805 -0.2037 0.0117 .
## continentEurope 0.0176 0.0667 0.2635 0.7921 -0.1132 0.1483
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4743 -2.9485 3.0515 -2.9485 27.0515
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0550
## I^2 (residual heterogeneity / unaccounted variability): 98.72%
## H^2 (unaccounted variability / sampling variability): 78.29
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.2939, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0017, p-val = 0.9667
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0742 0.2381 -0.3118 0.7552 -0.5408 0.3923
## mean.age -0.0002 0.0042 -0.0417 0.9667 -0.0084 0.0081
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8379 -5.6757 0.3243 -5.6757 24.3243
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0102
## I^2 (residual heterogeneity / unaccounted variability): 51.53%
## H^2 (unaccounted variability / sampling variability): 2.06
## R^2 (amount of heterogeneity accounted for): 93.26%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.0630, p-val = 0.1509
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 20.8789, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0820 0.0374 2.1926 0.0283 0.0087 0.1554 *
## scale1 -0.0175 0.0038 -4.5693 <.0001 -0.0250 -0.0100 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.5876 -7.1753 -3.1753 -5.7890 8.8247
##
## tau^2 (estimated amount of total heterogeneity): 0.0016 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0397
## I^2 (total heterogeneity / total variability): 97.06%
## H^2 (total variability / sampling variability): 33.99
##
## Test for Heterogeneity:
## Q(df = 2) = 97.5929, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0847 0.0235 -3.6052 0.0003 -0.1307 -0.0386 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4936 -2.9871 3.0129 -2.9871 27.0129
##
## tau^2 (estimated amount of residual heterogeneity): 0.0029 (SE = 0.0042)
## tau (square root of estimated tau^2 value): 0.0541
## I^2 (residual heterogeneity / unaccounted variability): 98.97%
## H^2 (unaccounted variability / sampling variability): 97.46
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 97.4647, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0675, p-val = 0.7951
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0966 0.0557 -1.7342 0.0829 -0.2058 0.0126 .
## continentEurope 0.0176 0.0677 0.2597 0.7951 -0.1151 0.1502
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4601 -2.9203 3.0797 -2.9203 27.0797
##
## tau^2 (estimated amount of residual heterogeneity): 0.0031 (SE = 0.0045)
## tau (square root of estimated tau^2 value): 0.0558
## I^2 (residual heterogeneity / unaccounted variability): 98.76%
## H^2 (unaccounted variability / sampling variability): 80.83
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 80.8303, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0015, p-val = 0.9695
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0755 0.2413 -0.3130 0.7543 -0.5486 0.3975
## mean.age -0.0002 0.0043 -0.0383 0.9695 -0.0085 0.0082
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8094 -5.6189 0.3811 -5.6189 24.3811
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0003)
## tau (square root of estimated tau^2 value): 0.0108
## I^2 (residual heterogeneity / unaccounted variability): 54.67%
## H^2 (unaccounted variability / sampling variability): 2.21
## R^2 (amount of heterogeneity accounted for): 92.64%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2059, p-val = 0.1375
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 19.6293, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0832 0.0390 2.1360 0.0327 0.0069 0.1596 *
## scale1 -0.0177 0.0040 -4.4305 <.0001 -0.0255 -0.0098 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.6265 -7.2531 -3.2531 -5.8668 8.7469
##
## tau^2 (estimated amount of total heterogeneity): 0.0015 (SE = 0.0016)
## tau (square root of estimated tau^2 value): 0.0389
## I^2 (total heterogeneity / total variability): 96.99%
## H^2 (total variability / sampling variability): 33.21
##
## Test for Heterogeneity:
## Q(df = 2) = 92.6322, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0871 0.0230 -3.7846 0.0002 -0.1322 -0.0420 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.2291 -6.4582 -2.4582 -5.0719 9.5418
##
## tau^2 (estimated amount of total heterogeneity): 0.0020 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0443
## I^2 (total heterogeneity / total variability): 84.02%
## H^2 (total variability / sampling variability): 6.26
##
## Test for Heterogeneity:
## Q(df = 2) = 13.8450, p-val = 0.0010
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2503 0.0281 -8.9043 <.0001 -0.3054 -0.1952 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5270 -3.0541 2.9459 -3.0541 26.9459
##
## tau^2 (estimated amount of residual heterogeneity): 0.0027 (SE = 0.0039)
## tau (square root of estimated tau^2 value): 0.0523
## I^2 (residual heterogeneity / unaccounted variability): 98.92%
## H^2 (unaccounted variability / sampling variability): 92.62
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 92.6211, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0983, p-val = 0.7538
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1011 0.0539 -1.8741 0.0609 -0.2068 0.0046 .
## continentEurope 0.0205 0.0655 0.3136 0.7538 -0.1078 0.1489
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2976 -2.5951 3.4049 -2.5951 27.4049
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0062)
## tau (square root of estimated tau^2 value): 0.0636
## I^2 (residual heterogeneity / unaccounted variability): 92.69%
## H^2 (unaccounted variability / sampling variability): 13.68
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 13.6766, p-val = 0.0002
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0228, p-val = 0.8801
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2578 0.0684 -3.7680 0.0002 -0.3919 -0.1237 ***
## continentEurope 0.0125 0.0828 0.1509 0.8801 -0.1499 0.1749
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.4824 -2.9648 3.0352 -2.9648 27.0352
##
## tau^2 (estimated amount of residual heterogeneity): 0.0030 (SE = 0.0043)
## tau (square root of estimated tau^2 value): 0.0546
## I^2 (residual heterogeneity / unaccounted variability): 98.73%
## H^2 (unaccounted variability / sampling variability): 78.55
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 78.5499, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0078, p-val = 0.9296
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0665 0.2361 -0.2816 0.7783 -0.5292 0.3962
## mean.age -0.0004 0.0042 -0.0884 0.9296 -0.0085 0.0078
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.2828 -2.5655 3.4345 -2.5655 27.4345
##
## tau^2 (estimated amount of residual heterogeneity): 0.0041 (SE = 0.0064)
## tau (square root of estimated tau^2 value): 0.0643
## I^2 (residual heterogeneity / unaccounted variability): 91.77%
## H^2 (unaccounted variability / sampling variability): 12.15
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1479, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0045, p-val = 0.9467
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2683 0.2871 -0.9345 0.3501 -0.8309 0.2944
## mean.age 0.0003 0.0051 0.0668 0.9467 -0.0096 0.0103
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.0391 -6.0782 -0.0782 -6.0782 23.9218
##
## tau^2 (estimated amount of residual heterogeneity): 0.0000 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0.0063
## I^2 (residual heterogeneity / unaccounted variability): 29.89%
## H^2 (unaccounted variability / sampling variability): 1.43
## R^2 (amount of heterogeneity accounted for): 97.35%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.4264, p-val = 0.2323
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 38.9987, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0814 0.0281 2.8905 0.0038 0.0262 0.1365 **
## scale1 -0.0178 0.0028 -6.2449 <.0001 -0.0234 -0.0122 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.3580 -4.7159 1.2841 -4.7159 25.2841
##
## tau^2 (estimated amount of residual heterogeneity): 0.0001 (SE = 0.0007)
## tau (square root of estimated tau^2 value): 0.0117
## I^2 (residual heterogeneity / unaccounted variability): 25.98%
## H^2 (unaccounted variability / sampling variability): 1.35
## R^2 (amount of heterogeneity accounted for): 93.07%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 1.3510, p-val = 0.2451
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 8.6026, p-val = 0.0034
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0483 0.0735 -0.6572 0.5110 -0.1923 0.0957
## scale1 -0.0212 0.0072 -2.9330 0.0034 -0.0354 -0.0070 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Linear with gender interaction model
Meta analysis
Age effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.7059 -7.4117 -3.4117 -6.0254 8.5883
##
## tau^2 (estimated amount of total heterogeneity): 0.0014 (SE = 0.0015)
## tau (square root of estimated tau^2 value): 0.0370
## I^2 (total heterogeneity / total variability): 93.41%
## H^2 (total variability / sampling variability): 15.17
##
## Test for Heterogeneity:
## Q(df = 2) = 45.6253, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.0958 0.0226 -4.2301 <.0001 -0.1401 -0.0514 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 2.8847 -5.7694 -1.7694 -4.3831 10.2306
##
## tau^2 (estimated amount of total heterogeneity): 0.0026 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0509
## I^2 (total heterogeneity / total variability): 81.98%
## H^2 (total variability / sampling variability): 5.55
##
## Test for Heterogeneity:
## Q(df = 2) = 13.4925, p-val = 0.0012
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## -0.2456 0.0343 -7.1626 <.0001 -0.3128 -0.1784 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Random-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 3.9531 -7.9061 -3.9061 -6.5198 8.0939
##
## tau^2 (estimated amount of total heterogeneity): 0.0005 (SE = 0.0008)
## tau (square root of estimated tau^2 value): 0.0227
## I^2 (total heterogeneity / total variability): 73.06%
## H^2 (total variability / sampling variability): 3.71
##
## Test for Heterogeneity:
## Q(df = 2) = 5.2178, p-val = 0.0736
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.0245 0.0158 1.5473 0.1218 -0.0065 0.0554
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with continent
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5745 -3.1489 2.8511 -3.1489 26.8511
##
## tau^2 (estimated amount of residual heterogeneity): 0.0025 (SE = 0.0036)
## tau (square root of estimated tau^2 value): 0.0495
## I^2 (residual heterogeneity / unaccounted variability): 97.68%
## H^2 (unaccounted variability / sampling variability): 43.19
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 43.1890, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0509, p-val = 0.8214
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0851 0.0539 -1.5777 0.1146 -0.1907 0.0206
## continentEurope -0.0146 0.0645 -0.2257 0.8214 -0.1410 0.1119
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3471 -2.6943 3.3057 -2.6943 27.3057
##
## tau^2 (estimated amount of residual heterogeneity): 0.0036 (SE = 0.0056)
## tau (square root of estimated tau^2 value): 0.0603
## I^2 (residual heterogeneity / unaccounted variability): 91.75%
## H^2 (unaccounted variability / sampling variability): 12.12
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 12.1193, p-val = 0.0005
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2574, p-val = 0.6119
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.2057 0.0848 -2.4249 0.0153 -0.3719 -0.0394 *
## continentEurope -0.0486 0.0958 -0.5073 0.6119 -0.2363 0.1391
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0 (SE = 0.0002)
## tau (square root of estimated tau^2 value): 0
## I^2 (residual heterogeneity / unaccounted variability): 0.00%
## H^2 (unaccounted variability / sampling variability): 1.00
## R^2 (amount of heterogeneity accounted for): 100.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 0.4999, p-val = 0.4795
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 4.7179, p-val = 0.0299
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0262 0.0272 -0.9626 0.3357 -0.0795 0.0271
## continentEurope 0.0606 0.0279 2.1721 0.0299 0.0059 0.1153 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with mean age
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.6484 -3.2968 2.7032 -3.2968 26.7032
##
## tau^2 (estimated amount of residual heterogeneity): 0.0021 (SE = 0.0031)
## tau (square root of estimated tau^2 value): 0.0457
## I^2 (residual heterogeneity / unaccounted variability): 96.41%
## H^2 (unaccounted variability / sampling variability): 27.88
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 27.8790, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.2292, p-val = 0.6321
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.1929 0.2056 -0.9382 0.3481 -0.5958 0.2101
## mean.age 0.0018 0.0037 0.4787 0.6321 -0.0054 0.0089
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.5103 -3.0205 2.9795 -3.0205 26.9795
##
## tau^2 (estimated amount of residual heterogeneity): 0.0024 (SE = 0.0040)
## tau (square root of estimated tau^2 value): 0.0489
## I^2 (residual heterogeneity / unaccounted variability): 83.68%
## H^2 (unaccounted variability / sampling variability): 6.13
## R^2 (amount of heterogeneity accounted for): 7.74%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 6.1268, p-val = 0.0133
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.7924, p-val = 0.3734
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.4901 0.2762 -1.7744 0.0760 -1.0315 0.0513 .
## mean.age 0.0045 0.0051 0.8902 0.3734 -0.0054 0.0145
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0002 (SE = 0.0005)
## tau (square root of estimated tau^2 value): 0.0138
## I^2 (residual heterogeneity / unaccounted variability): 55.26%
## H^2 (unaccounted variability / sampling variability): 2.23
## R^2 (amount of heterogeneity accounted for): 62.87%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2349, p-val = 0.1349
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 2.8308, p-val = 0.0925
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1966 0.1009 1.9486 0.0513 -0.0011 0.3943 .
## mean.age -0.0032 0.0019 -1.6825 0.0925 -0.0069 0.0005 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Meta analysis with scale
Age effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.9326 -3.8653 2.1347 -3.8653 26.1347
##
## tau^2 (estimated amount of residual heterogeneity): 0.0010 (SE = 0.0017)
## tau (square root of estimated tau^2 value): 0.0314
## I^2 (residual heterogeneity / unaccounted variability): 80.51%
## H^2 (unaccounted variability / sampling variability): 5.13
## R^2 (amount of heterogeneity accounted for): 27.85%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 5.1296, p-val = 0.0235
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.5190, p-val = 0.2178
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0245 0.0999 0.2454 0.8061 -0.1713 0.2203
## scale1 -0.0126 0.0102 -1.2325 0.2178 -0.0326 0.0074
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.3241 -2.6482 3.3518 -2.6482 27.3518
##
## tau^2 (estimated amount of residual heterogeneity): 0.0023 (SE = 0.0059)
## tau (square root of estimated tau^2 value): 0.0478
## I^2 (residual heterogeneity / unaccounted variability): 55.24%
## H^2 (unaccounted variability / sampling variability): 2.23
## R^2 (amount of heterogeneity accounted for): 11.62%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 2.2340, p-val = 0.1350
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.8570, p-val = 0.3546
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.0989 0.1625 -0.6085 0.5429 -0.4174 0.2196
## scale1 -0.0156 0.0168 -0.9258 0.3546 -0.0485 0.0174
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age \(\times\) Gender effect results
##
## Mixed-Effects Model (k = 3; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## 1.7678 -3.5355 2.4645 -3.5355 26.4645
##
## tau^2 (estimated amount of residual heterogeneity): 0.0013 (SE = 0.0024)
## tau (square root of estimated tau^2 value): 0.0362
## I^2 (residual heterogeneity / unaccounted variability): 76.89%
## H^2 (unaccounted variability / sampling variability): 4.33
## R^2 (amount of heterogeneity accounted for): 0.00%
##
## Test for Residual Heterogeneity:
## QE(df = 1) = 4.3264, p-val = 0.0375
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.5037, p-val = 0.4779
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1029 0.1177 0.8744 0.3819 -0.1278 0.3337
## scale1 -0.0085 0.0120 -0.7097 0.4779 -0.0321 0.0151
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16. Meta analytic summary of slope estimates
Intercept-only model
Fixed effect model
Linear model
Linear with gender model
Linear with gender interaction model
Quadratic model
Quadratic with gender model
4.7. Social risk-taking